Automated diagnostic system and method including synergies6767325Abstract Structure-based processing includes a method of diagnosing diseases that works by arranging diseases, symptoms, and questions into a set of related disease, symptom, and question structures, such as objects or lists, in such a way that the structures can be processed to generate a dialogue with a patient. A structure-based processing system organizes medical knowledge into formal structures and then executes those structures on a structure engine to automatically select the next question. Patient responses to the questions lead to more questions and ultimately to a diagnosis. An object-oriented embodiment includes software objects utilized as active, intelligent agents where each object performs its own tasks and calls upon other objects to perform their tasks at the appropriate time to arrive at a diagnosis. Alternative symptoms, synergies, encoding of patient responses, multiple diagnostic modes, disease profiles or timelines, and the reuse of diagnostic objects enhance the processing of the system and method. Claims What is claimed is: Description RELATED APPLICATIONS
Answerability probability that the patient knows the symptom
Class what kind of symptom this is (history, sign, custom,
logic.)
Documentation description and development history of the symptom
object
ICD ICD-9CM code for the symptom
Keywords search words to find symptom object in the index
Label name of the symptom object (not the symptom)
Location where the symptom can be obtained
Name formal medical name of the symptom
Onset_Offset special onset/offset attributes
Persistence how long a value is good for, once obtained
SNOMED classification code for indexing the entire medical
vocabulary, including signs, symptoms, diagnoses, and
procedures
Synonyms alternate names of the symptom
Trending special trending information such as the changes in
severity of a symptom with time or the evolution of
symptoms in a disease process
Valuator label of the object that actually obtains the symptom
values
Value current value of symptom
Value_Date date of last Value
Value_Time time of last Value
Value_Type operational type of the value (integer, real, text, discrete)
C. Valuator Object A Valuator Object (VO) is a software object that represents the actions required to establish the value of a symptom in a patient at a specified time. The VO data are the basic properties of the symptom and its runtime state flags, such as: the type of valuation used (question, formula, graph, table), the type of value reported (numeric, words, graphic), the valid symptom values (NONE, LOW, MEDIUM, HIGH), if applicable, the question object to be used, if applicable, the mathematical or logical formula used, if applicable, the graph, or table, or database to be used. The VO actions are the functions and procedures needed by the system to manipulate a value, such as processes to: pre-test the valuator, print the valuator formulas for review/editing by the author, establish the value in the current patient, report the value. The basic use of the VO is as an interface between the symptom and the patient level of abstraction. The VO can be used to present dummy patients to the LB system for testing. The VO can be used to switch among lookup tables, based on global system control setting. The VO makes an object out of an action, a common use of objects, so that we can globally describe and control the actions that take place at some lower level. D. Question Object A Question Object (QO) is a software object that describes the software elements required to establish a mini-dialog of questions and responses with the patient, in order to obtain a symptom value. It is the task of the QO to select the appropriate question set, to invoke the appropriate node objects that actually question the patient, and to report back the patient's response. A QO is a type of valuator object that specializes in interaction with a patient. The Question Object is the point in defining a script where the author actually writes a script, albeit typically a very short one, that is focused on asking about one specific symptom. This mini-script is broken down into separate node objects, each of which presents a Preamble, a Question, and a set of labeled Buttons to the patient, and obtains a response from the patient. The QO data are those elements required to ask a question and obtain a response from a patient, such as the list of node objects to be used. The object's actions are the functions and procedures needed by the system to manipulate a question, such as processes to: pre-test the question and node elements, print the question elements for review/editing by the author, ask the question and report the response, specify the actual natural language text to be used, establish the user interface required for the current platform, invoke a node object to actually ask the question and report the response. The QO is another interface object, used to separate the questioner from the language used to question the patient. The basic use of the QO is to handle the details required to present a (possibly complex) question to an online patient. The QO can be used to change the educational level of the question text (Question Roller). The QO can be used to change natural language used to speak to the patient. E. Node Object A Node Object (NO) is a software object that describes the software elements required to ask a single, well-defined question of the patient and to return the response selected by the patient. It is the task of the NO to present the required data to the GUI in a form that will appear user-friendly manner on the user display, to wait an appropriate amount of time for a user response, to possibly re-prompt the user, and to ultimately return the user's response. Node objects operate at the lowest level of the script hierarchy; they interface to the operating system's user interface. Computation depends on the platform used. For a Windows operating environment, the node would display an appropriate window containing sub-windows for the Preamble and Question Text. Next it would display the requisite number of buttons and display the form to the user. When a button is pressed by the user, the node object returns the index number of the response. The NO data are those elements required to ask one detailed question, obtain a response, and return the index number of the response. The NO's actions are the functions and procedures needed by the system to display a question to the user, such as processes to: pre-test the node elements, print the node elements for review/editing by the author, display the question and report the response. The NO is another interface, between the script objects and the patient. The basic use of the NO is to handle the low-level details required to "talk" to a patient. The NO can be used to port an application to another hardware platform or operating system. The NO can be used to "fake" a patient by taking inputs from a test file and writing outputs to a test result file. The NO can be used to log all questions to, and responses from, the patient, time-stamped to the nearest hundredths of a second if necessary. One of the reasons for defining Node Objects as well as Questions Objects is that the entire system can be translated into other languages by translating all of the Node Objects. IV. List-Based Engine Concepts In one embodiment of the invention, the List-Based Engine (LBE) is one embodiment of the diagnostic processing method. It is a program that, essentially, takes a set of diseases (more precisely a collection of disease descriptions, symptom definitions, and question specifications) and processes them against one specific patient. The patient is typically a human that can conduct an interactive dialog with the system and can respond to questions posed by the system. Alternatively, the patient may be represented by a medical record in which some or all symptoms already have values, so that the system simply sifts the values and scores the diseases accordingly. For test purposes, the patient may even be represented by a computer program that is "playing patient" in order to test the system's ability to respond to abnormal situations such as unexpected key presses, extensive response delays, contradictory answers, requests for repeating a question, and abnormal termination of a session. For a specific run or session, the system begins its work by gathering a set of candidate diseases that it is supposed to diagnose. This initial candidate list is most likely assembled by a module that has analyzed the patient's Chief Complaint and selected appropriate diseases from a database that is indexed by chief complaint. In the absence of a chief complaint, the system can just start with all the diseases it finds in a given project file, where an author wants to test a newly created or edited script. Once it has a list of candidate diseases, the system's job is to process these diseases, typically by asking questions and accumulating diagnostic scores for each disease until some specified system goal is reached. This system goal is expressed by the system "Mission" setting, which can specify various goals such as "run all diseases" or "run until the first disease is ruled in" or "run until 10 minutes have passed", and so on. The default system mission is to "run all diseases until all symptoms have been evaluated". Diagnostic Loop In one embodiment, the system uses a "diagnostic loop" to process the current disease list. Portions of the diagnostic loop have been described in Applicant's U.S. Pat. No. 5,935,060, which is hereby incorporated by reference. The diagnostic loop consists of a series of iterations in which the system considers its mission in the light of the latest status of all candidate diseases. Depending on the mission, the system can perform all kinds of special calculations and evaluations during this loop. The loop actually consists of several nested loops that may involve recursion to evaluate subordinate symptoms. Current Disease In one embodiment, during the diagnostic loop, the first aim of the system is to determine which disease it should evaluate next, based on its mission. The mission might be to "evaluate the disease with the highest score", or "evaluate the disease with the highest diagnostic momentum", or "evaluate any random disease". The default mission is to evaluate the next disease as originally given in the candidate list. Current Symptom In one embodiment, once it has a "current disease", the next system aim is to determine which symptom of the current disease it should evaluate next. The mission might be to "evaluate the symptom that can add the highest weight to the score of the current disease". A more complex mission might be to "evaluate the symptom that will advance the score of the most diseases". The default mission is to evaluate the next symptom in the symptom list of the current disease. Current Evaluation In one embodiment, evaluating a symptom consists of establishing the value of the symptom for a specified date and time in the patient's life. How this is done depends on the type of symptom and on the type of the valuator object defined for the symptom. A symptom may already have a valid current value in the patient's medical record. For example, the patient's gender may already be in the medical record, in which case the system obtains it and continues. The patient may already have supplied the symptom value during the current session in the context of a question for some other disease. Again, the system obtains the symptom value from the current session record. (This feature avoids asking the patient the same question in the process of evaluating different diseases.) Many symptoms are evaluated by running a Question object, i.e., asking the patient one or more questions. Symptoms may use a Logic object to evaluate a value; this means that the system parses and runs a logic formula, such as "if patient has symptom value A and has symptom value B, then the value of this symptom is C". To evaluate this symptom, the system would (recursively) evaluate symptoms A and B and then establish C, if appropriate. Scoring In one embodiment, after the system establishes a new symptom value, it updates the scores of all candidate diseases. Depending on the description of each disease, scoring can consist of simply adding the weight corresponding to the new current symptom value, or it can involve adding special synergy weights based on the values of other symptoms, or on the timing of symptoms. Scoring can also include establishing probabilities of diagnosis, which typically depend on the existence of several symptom values, sometimes in a defined time order. Finally, scoring includes evaluating the scores of diseases against various thresholds. Depending on the system goals, a disease may be placed into a special category based on its score. For example, a disease may be deemed "ruled in" when its score reaches or exceeds a specified threshold, or it may be placed on a special diagnostic momentum track if its score is increasing more rapidly than other disease scores. The default system goal is to add the symptom weights to all applicable disease scores. Continuation In one embodiment, after the system has updated the scores of all diseases, it determines how to continue by considering the set of new scores. Again, the system's goals can specify different actions for the system, such as "stop when any score exceeds 1000" or "stop when the diagnosis has been ruled in" or "stop when the system has the five most likely diagnoses" or "stop when ten minutes have elapsed". The default goal is to run until all symptoms of all diseases have been evaluated. A. Dynamic Rules and Goals In one embodiment, the system is designed so that the rules, limits, and goals that govern the diagnosis can be changed at run time. The system may use tables of rules and goals and limits, of which the applicable set is selected as needed. For example, at the top of the diagnostic loop when the system selects the next disease to be considered, it can use any one of a number of rules such as "Select the disease that: is the most life-threatening disease remaining to be diagnosed, shares the most symptoms with other diseases, has the highest current diagnostic score, has the highest current change in diagnostic score, has the fewest unresolved symptoms, is next in some order specified by the author." Similarly, when the system selects the next symptom with a disease, it can choose it based on various dynamic modes or control variables. The patient him/herself can set certain boundary conditions on the consultation. Several examples include: a patient who has only 20 minutes to talk a patient who wants only to exclude a certain disease ("e.g., my friend had a headache like mine, and he was diagnosed with brain tumor") B. Diagnostic Momentum In one embodiment, the "diagnostic momentum" is the rate of change of the diagnostic score of a candidate disease. It provides a measure of how fast a given disease is accumulating diagnostic weights, compared to other competing candidate diseases. The system tracks the score and the momentum for all candidate diseases and can use this information to change the diagnostic mode. Note that the use of various synergy weights will add extra weight to diseases with many matching symptoms, so that positive feedback is established that tends to favor diseases with many matching symptoms and thus to converge rapidly on one disease (see e.g., Sequencing Synergy and Summation Synergy). As the LB method diagnoses, it tracks for each disease the latest diagnostic score, the last change in the score, and the name of the disease with the largest momentum during the current iteration of the diagnostic loop. Since the name of the disease with the highest momentum is available at all times to the LB engine, it can be used to guide the diagnostic process itself and to check whether any goals or limits or decision points have been reached. It provides the LB method with feedback that lets it feel its way along a diagnostic path in a manner that is strongly driven by the patient's responses. For example, the faster a disease is approaching the diagnostic threshold, the more intensely the LB method can focus on disease. This feature simulates the manner in which a human doctor sifts his knowledge of disease based on what s/he is learning about the patient's condition. As the symptom pattern begins to match the pattern of a specific disease, the doctor will ask questions designed to confirm (or reject) this disease. The advantage of the momentum feature is that it (1) quickly de-emphasizes many less relevant diseases, (2) minimizes questions asked of the patient, and (3) cannot be done as rapidly and as precisely by the human doctor as by the computer. C. Horizontal Axis of Inquiry (HAI) In one embodiment, the system conducts its diagnostic inquiries along various "axis", i.e., lines of investigation or focus directions. We call two of these strategies the Horizontal Axis of Inquiry (HAI) and the Vertical Axis of Inquiry (VAI). This section focuses on the HAI. Note: The "inquiry axis" terminology relates to the manner in which the system selects the next focus symptom. The terminology derives from the Disease/Symptom Matrix (DSM) metaphor, in which a table is formed by arranging the candidate diseases as side-by-side columns (hence "vertical") and the component symptoms as rows (hence "horizontal"). See the DSM figure. In database terminology, fields are arranged along the vertical, and records arranged along the horizontal. The Horizontal Axis of Inquiry (HAI) strategy is a diagnostic mode that focuses on quickly eliminating inapplicable diseases from a large list of candidates. HAI is typically used early in a diagnostic session, when the system has numerous candidate diseases and selects focus symptoms based more on how many diseases contain the symptom than on how effective the symptom will be in identifying one disease. Other diagnostic methods have one and only one method. The present invention, by contrast, allow many different modes of inquiry, which are themselves dependent on the progress of the diagnosis. In both the HAI and VAI strategies, the LB engine updates the scores of all candidate disease scores with the responses obtained from the patient. Thus, the differences in these strategies relate primarily to how the system selects the next focus symptom, not how the candidate disease scores are updated. In the HAI mode, the Alternative Symptom (AS) feature will typically be activated, so that fewer and more general questions tend to be asked. In the VAI mode, the AS feature may or may not be activated, depending on the need for more detailed responses from the patient. The choice between HAI and VAI strategies is very important because it permits general "sifting" of many candidate diseases as well as focusing on diagnosis of one specific disease, at a detail level where the patient can--indirectly--interact with the script author, a world specialist on that disease. Other medical diagnostic systems typically interact at one and only one level with the patient. The decision which one of these (or some other) strategies or modes to select can be programmed to depend on any number of variables. For example: it may be specified by the process that calls the system; it may be modified based on the goal selection routine that runs early in the consultation; it may be switched based on the Diagnostic Score or Momentum reached by one or more diseases; it may be switched by various computations related to the Chief Complaint or the First Significant Symptom; it may be switched based on a new response by the patient, which negates or significantly modifies a previous response. In the HAI strategy, the system searches the list of candidate diseases and their symptom lists to find symptoms shared by many diseases. It selects such a shared symptom and evaluates it, typically by asking a question, or by evaluating a formula or a logic structure. Then it updates every disease with the new value of the symptom, and adds the appropriate weights to each disease score. In the HAI strategy, the system can sort the candidate diseases by the number of shared symptoms to prepare for an efficient subsequent elimination process. For example, by establishing the gender of the patient, the system can eliminate all gender-specific disease. The HAI strategy permits the system to partition the candidate diseases into useful classes so that it can focus on promising classes first. For example, it might partition diseases into the following categories: urgent, serious, common, or it might partition diseases into promising (high probability that the diagnosis is among them), intermediate and low probability. D. Vertical Axis of Inquiry (VAI) The Vertical Axis of Inquiry (VAI) strategy is used to examine one candidate disease in detail, so that the system selects the next focus symptom repeatedly from the same disease. This strategy is intended to give one specific disease that has scored significantly the chance of establishing itself as a diagnosis. The VAI strategy is equivalent to letting the script author (1) ask several successive questions about this disease and (2) ask his or her preferred questions, in cases where the patient has previously answered Alternative Symptoms. In the VAI strategy, the LB engine evaluates the various symptoms of one disease. Symptoms can be selected in various orders, depending on the engine mode. In one embodiment, the script author may prescribe a sequence in which symptoms are evaluated, but this can be overridden by first asking for symptoms that carry the most weight, or for symptoms that are the easiest or fastest for the patient to answer. The system selects such a shared symptom and evaluates it, typically by asking a question, or by evaluating a formula or a logic structure. Then it updates every disease with the new value of the symptom, and adds the appropriate weights to each disease score. In the VAI strategy, a patient who has earlier answered questions using Alternative Symptoms (see there) can now have the opportunity to be asked the symptoms which the author defined. This has the effect of "fine-tuning" the answers to the specific disease at the point when the disease is becoming a contender. In this way, a patient can be promised that no matter what disease they have (if the system covers that disease) they can be guaranteed to interact with a dialogue created by a world-class specialist in that disease. The VAI strategy can be set to use only the author's own symptoms, instead of accepting the (normally alternative) symptoms of other authors. This means that the system can (perhaps at the patient's request) re-ask all symptoms using only the authors' own questions. This, in turn, means that the patient's entire consultation on a given disease can ultimately be conducted using the world expert on the disease. This gives the LB method the ability to shift from the broad, generalizing viewpoint (where it accepts all alternative symptoms) to the narrow, specific viewpoint, where the world expert's questions phrasing may help to distinguish among close diseases. The HAI and VAI strategies are part of the central processes for symptom selection of the system, specifically the LB Diagnostic Loop. The decision which one of these (or some other) strategies or modes to select can be programmed to depend on any number of variables. For example, it may be specified by the process that calls the LB engine; it may be switched based on the Diagnostic Score or Momentum reached by one or more diseases; it may be switched by various computations related to the Chief Complaint or the First Significant Symptom; it may be switched based on a new response by the patient, which negates or significantly modifies a previous response. The VAI and HAI strategies permit the system to vary its diagnostic focus from the general to the specific. In the early stages, the engine knows little about the patient and must ask the best general questions that quickly eliminate large numbers of candidate diseases. But after applying the HAI strategy for a while, if the diagnostic momentum of some disease D reaches a specified level, the engine can then switch to the VAI strategy to focus diagnosis on disease D, to the momentary exclusion of all other diseases. It is important to note that all of the disease object (experts) "monitor" all of the questions and answers generated by other disease objects. After applying VAI for a while, disease D may emerge as the "front runner", or it may fade, being outstripped by one or more other disease scores. One of these may then become the driver of another VAI round, or the diagnostic strategy may revert to HAI if no disease has a clear lead. There is a powerful holistic effect when various LB features such as Disease Momentum, Dynamic Goals, HAI, VAI, Alternative Symptoms, and Synergy Weighting are combined. Consider how the LB engine sifts the candidate diseases and converges on the appropriate ones: As one disease gains score and momentum in the HAI strategy, this triggers a shift to VAI strategy. If the system is "on the right track", the VAI strategy will rapidly confirm that several key symptoms of that disease are present in the patient. Through the various Synergy weights, this confirmation will increase the score and the momentum, and reinforce the cycle to converge on the focus disease as a diagnosis. In one embodiment, the symptom weights may be increased when the system is operating using the VAI strategy. This feature allows the system to accommodate Bayesian probabilities in the evaluation process. On the other hand, if the system is "on a cold trail", the VAI strategy will fail to confirm additional symptoms, the disease score will lag behind those of other diseases (which are being updated in parallel) and the system will soon abandon this fruitless pursuit and either return to the HAI strategy, or select another disease for a VAI inquiry. E. Alternative Symptoms In one embodiment, the Alternative Symptom feature of the LB method lets a disease author specify a set of symptoms that are alternative to a specified symptom for the purpose of diagnosis. The invention lets an author specify alternative symptoms that can take the place of the author's preferred or specified symptom, perhaps with a different weight. The feature is designed to solve the problem that different authors may prefer different ways of asking a patient about the same symptom, yet we do not want the patient to have to answer questions about the same symptom over and over. The LB engine is programmed with alternate modes that either do or do not permit symptom alternatives. When permitted, the system accepts the value of any alternative symptom as the value of a symptom; if not permitted, the system requires the asking of the author's specified symptom, even if this requires asking the patient certain questions a second time. One goal of the LB diagnostic method is that, no matter which disease the patient has, he or she will be asked questions by a world-class specialist in that disease. This feature mimics how human doctors interview a patient: In the early part, the doctor asks broad questions that determine the general overall nature of the patient's disorder. Once one disease emerges as a likely diagnosis, the doctor asks more specific questions that confirm or reject the hypothesis to some degree. Finally, when the most likely diagnosis seems almost obvious, the doctor asks even more detailed questions in order to repeat, emphasize, seek more details, add confirming symptoms, and so on. These final question may well repeat questions asked earlier, perhaps to give the patient a final chance to confirm earlier responses. The Alternative Symptoms feature gives the patient the option to go back and answer questions exactly as worded by the original disease script author, or to simply accept the questioning by the alternative symptoms. This is analogous to a computer user who installs an application who can either insist on a "custom installation" or accept the "typical installation". At scripting time, when the author of a disease script first lists the component symptoms of the disease, the author can either specify brand new symptoms, which the author writes from scratch, or specify existing symptoms, which the author retrieves from a database of stored or archived symptoms that is shared by all authors. This initial symptom set becomes the author's preferred or specified symptoms, which the author prefers to be asked of the patient. Next, the author reviews the symptom database to see which symptoms are so "close" to his/her specified symptoms that they can serve as alternatives. The author lists these alternative symptoms and assigns some diagnostic weight to them. One author's specified symptom is another author's alternative symptom. Thus all symptoms are specified symptoms to some author, who is responsible for maintaining their currency. Each of the authors may be linked by a data communications network such as the Internet. When a new symptom object is created by Author A, a copy of the new symptom object is instantly "sent" to the authors of the diseases in which his symptom is also used, e.g., Author B. This then would be an alternative symptom for Author B. Author B then assigns a weight for the disease he is authoring when this new alternative symptom is used in a question. At run time, the system can either allow or disallow the use of alternative symptoms. If the system is in the alternative symptom mode, and the system is seeking the value of specified symptom S1, it may accept the value of any alternative symptom in its place. The effect is that, if the patient has already been asked about any alternative symptom S2, S3, or S4, the system will not ask the patient again, but will accept the alternative symptom and its weight. If the system is not in the alternative symptom mode, when the system seeks the value of specified symptom S1, it will proceed to ask the questions associated with symptom S1. The Alternative Symptom feature eliminates redundant questioning of the patient and permits the author to group symptoms together that have the same impact on his disease. The Alternative Symptom feature lets the author control how she or he wants to focus on symptom details, i.e., on the quantization of symptoms. For high-level diagnosis, a high level of quantization may be sufficient; at a later time, the author may need more precise details, such as to distinguish between close variants of a disease. In one embodiment, the system symptom database may contain several thousand symptom script elements, written independently by several hundreds of authors. Many of these symptoms may be the same, or be acceptably similar variations of each other. Without Alternative Symptoms, the system would load all candidate diseases. In the course of running them, the engine might encounter some of these similar symptoms several times. The effect would be to ask the patient the same question in many different ways, which would be inefficient and would not engender confidence. But with the Alternative Symptom feature, after the system evaluates any one of the alternative symptoms, the other symptoms in the set will not be asked. A benefit of the object-based system having Symptom Objects and using the Alternative Symptom feature is that Symptom Objects and their underlying objects, e.g., Valuator Objects, Question Objects and Node Objects, can be "reused". In one embodiment, the author of a new disease script can reuse previously written and debugged objects by a few steps, which may include, for example, renaming one or more of the objects and assigning alternative weights. This object reuse capability permits faster coding, testing and release of new disease scripts. F. Disease Timeline In one embodiment of the invention, the Disease Timeline may be a chart or graph that describes how each symptom of the disease manifests itself over time in a typical patient. The timeline is a characteristic "pattern" of the disease that can be used as a reference for comparisons of the patient's actual symptom time chart. This aspect of the invention relates to pure medical knowledge about a disease; it is independent of any one patient. This aspect is "theoretical", in contrast to a Symptom Time Chart, which relates to the "actual" symptom values as experienced by a patient over time. The timeline is for a generic occurrence of the disease, to serve as a base reference. It can be scaled to fit a given patient. At design time, the author of a disease object describes the typical course of the disease in terms of how and when its symptoms typically arise (onset), vary, and subside (offset) over time. This timeline starts with the First Significant Symptom (FSS) of the disease, and all timings are based on the start of the FSS. Note that the FSS may be different than the patient's chief complaint. One embodiment utilizes a Gantt chart that records the times of the appearance, disappearance, overlap, and other aspects of the component symptoms. Initially, the author might only choose three time points for each symptom; later, more and more points can be added. A typical goal is an hour-by-hour description of the disease. At run time, the system matches the patient to the script. Appendicitis may be used as an example disease to walk through a simple diagnosis. Assume that the author has chosen to describe the disease as follows: The first symptom is often (though not always) anorexia, so this symptom is the origin for the timeline. Anorexia, then, occurs at 0 hour. At hour 1, one typically expects nausea. At hour 3, one expects epigastric pain to become noticeable to the patient. By hour 8, one can expect the pain to be migrating to the right lower quadrant of the abdomen, and so on. At run time, when a patient enters the system, the system preferably asks when the chief complaint started. In one embodiment, the system then selects the script that is nearest in time. So, here is a patient with appendicitis calling the diagnostic system; she or he may, of course, be at any stage along the disease timeline. Usually an appendicitis patient waits until she or he has abdominal pain before seeing a doctor. So, let's say our patient presents abdominal pain of a given severity as the chief complaint. The system (in HAI mode) then searches all candidate scripts for abdominal pain of our patient's severity. It finds the appendicitis script, which indicates where a patient with that severity should be placed along the time line. The disease object can now compute the time offset required to match the patient, and can "place" or "match" the patient to that point in time in the appendicitis script. Sooner or later, the LB system will let the appendicitis script ask another symptom. The script will ask the patient about earlier nausea or anorexia, and--if the patient confirms--will add weight to the score of appendicitis. At some point, the rising score will trigger the system to switch to VAI mode, and to ask about several more symptoms from the appendicitis script. This may rapidly pile on more weight, and the appendicitis diagnosis would then exceed threshold and would be ruled in. If not, the system will know what symptoms should appear next, and let the patient know. The chart, graph or timeline described above may also be referred to as a predetermined template of symptom characteristics. One or more of the established symptoms may have symptom characteristics that arise (onset) or subside (offset) over time so as to match the predetermined template. If so, additional weight is added to the score for the particular disease. Furthermore, if the onset or offset characteristics match the predetermined template and a set of the established symptoms occur in a specified sequence over time, still more additional weight is added to the score for the particular disease. Thus, it can be seen that when certain symptom conditions are met, the score of a particular disease may rapidly reach the disease threshold and be ruled-in or diagnosed. A disease needs time to "declare itself." On one hand, the longer one waits in a disease process, the more certain they can be of the diagnosis; on the other hand, one wants to make the diagnosis as soon as possible to begin appropriate treatment. The author actually has two "clocks". One clock is related to the appearance of the Chief Complaint, the other clock is related to the appearance of the First Significant Symptom. The HAI mode uses the CC clock, while the VAI mode uses the FSS clock, which is more accurate, but cannot be used until one has a tentative diagnosis. See FIG. 28 for an exemplary screen shot of a user interface for specifying the order of a particular set of symptoms so as to establish the First Significant Symptom. The user may for example, slide symptom bars along the time axis to indicate their particular symptom history. The user would then click on the "submit" button which causes the new symptom occurrence times to be captured and then evaluated by the system. The author can also use the symptom timeline as a characteristic pattern of symptom magnitudes. This is useful in describing and differentiating diseases based on their symptom patterns. G. Spectrum of Terms/PQRST Code In one embodiment of the invention, the PQRST Code is a comprehensive method for capturing and encoding a patient's verbal description of a symptom. It is particularly suitable for highly subjective symptoms that are hard to quantify, such as the patient's overall health, the characterization of a particular pain, or the expression of a mental state or emotion. The key invention here relates to the "Vocabulary of Diagnosis." This refers to the ability of the LB method to let an expert author use the exact vocabulary she or he has developed over years of experience in questioning the patient. In the real world, certain words used by patients to describe pain are classic indicators of specific disease. In the LB world, this is implemented by letting the patient select from a pick list of words that are then associated with a predetermined diagnostic weight. The PQRST Code may be used to track changes in other health data such as lesions, masses, discharges, body functions, mental states, emotions, habits, addictions, and so on. Pain is a subjective experience of the patient. It is diagnostically highly useful, yet practically very hard to describe in sufficiently useful detail. The PQRST Code is a comprehensive method for encoding a patient's description of pain, and for using the pain code for diagnosis in the LB method, and for other purposes such as Advice, Prescription, Treatment, Pain Management, and Disease Management. In the LB diagnostic method, the PQRST Code may be used to encode subjective symptom descriptions, to capture changes in symptom descriptions, and to analyze changes over time. Not only may the PQRST Code itself be composed of hundreds of elements, but the possible uses of the code in medical automation are manifold. The PQRST Code is directed to manipulating medical knowledge in an automated way. The basic idea is using word spectra and pick lists to capture a patient's subjective description of some health experience. The PQRST Code may then be used to detect symptom changes, slopes, trends, areas, and so on, where change is the key. The PQRST Code feature includes picking words from a word spectrum at two points in time, and then analyzing the significance of the change, and using this to give extra weight to one diagnosis. This feature places words in the spectra that do show how a particular aspect of pain is likely to change over time, and then does the second evaluation and gives extra weight to one diagnosis because it manifested the expected change. The PQRST Code feature includes methods for: describing some 20 aspects of pain, obtaining these aspects from the patient, encoding and decoding these aspects as a single PQRST code, using the PQRST code in diagnostic and other contexts. At the global level, for all authors and all script, we define some 20 aspects of pain, such as Quality, Severity, Location, Size, Symmetry, Timing, Localizability, and Migration aspects. For each aspect, we further define a word spectrum that consists of a set of words commonly used by patients to describe that aspect of pain. For example, the Quality of pain might be described in terms of "pinprick, knifing, tearing, fullness, tightness, pressure." The Severity of pain would be rated by the patient on a scale of 0 to 10. Word spectra are, of course, different for different aspects of a symptom. Non-pain symptoms might rank some aspect like "Age" along a numeric scale such as 0-7, 8-22, 23-65, and 66 and over. Another spectrum might use words such as NONE, LOW, MEDIUM, HIGH to characterize an aspect. Or, a word spectrum might consist of a vocabulary of descriptor words such as PULSING, POUNDING, HAMMERING, TAPPING. The script author defines diagnostic weights for each word of a spectrum. At run time, a given spectrum is presented as a pick list from which the patient can choose. The patient picks one word from the list, and the system adds the associated diagnostic weight to the score. The PQRST Code feature permits authors to apply the vocabulary of diagnosis that they have developed over years of experience. A script can use several word spectrum symptoms to build up a PQRST Code that summarizes the state of health of a patient at some time "t". This code can be stored in the patient medical record (PMR) for later use. This is another example where a symptom object specialized for word spectra may be defined. The script can collect a PQRST Code for different times T1, T2, T3. The script can then analyze the changes in code over time, and assign weights for significant symptom changes over time. The script can use the PQRST code to compute synergies based on slope, trend, area, volume, and other properties. Frequently during the same consultation, the severity of the patient's symptoms is trended. In addition, many PQRST array spectra can be asked at the beginning and at the end of the same consultation. The reenter function (second consultation for the same disease process) and the re-reenter function (third consultation for the same problem) are used in concert with the PQRST array to evaluate the evolution of the disease process to make the diagnosis. Each author is able to use or re-use the word spectra that are already created. Each spectrum is typically 7 to 11 adjectives that are carefully selected. For example, if a patient's epigastric pain (location) which cannot be localized (localizability) moves (migration) to the right lower quadrant (location) and now is easy to localize (localizability), the patient has appendicitis. The diagnostic system can collect and publish medical statistics on the "vocabulary" that is used in diagnosis. The diagnostic system can use the vocabulary as a "digitized medicine" to fine-tune scripts and their actions. The following is an example of PQRST Code that tracks the nature of a discharge, instead of a pain. The Mallory-Weiss syndrome consists of a partial thickness tear in the very inferior aspect of the esophagus. It is caused by severe vomiting. Hence a patient who is vomiting food at time "t" and vomiting food with blood in it one hour later has Mallory-Weiss syndrome, compared with a patient with a gastric ulcer, who would have blood in the vomit from the beginning. Therefore, a symptom built around PQRST encoding of vomit contents will detect the addition of blood and add the appropriate synergy weight to the Mallory-Weiss disorder. H. Synergy In one embodiment of the invention, and in the context of automated medical diagnosis, "synergy" means adding extra diagnostic weight to a disease if a symptom occurs in the patient in a specified manner, intensity, anatomic location, frequency, sequence, combination with other symptoms, or similar pattern. The synergy concept provides a way for an automated diagnostic system to take into account the symptoms of a patient viewed as a holistic pattern that can be used to incrementally refine the ranking of a disease for the purpose of diagnosis. The word "synergy" may have the meaning of "combined effect." It refers to accounting for the special additional impact on diagnosis of the fact that a symptom is occurring, changing, or interacting with other symptoms in the patient in some well-defined manner with respect to time, anatomic space, quality, sequence, frequency, combination, mutual causation, and so on. In short, the synergy concept implements in software the medical fact that the diagnostic significance of a combination of symptoms is much greater than the significance of each of the symptoms in isolation. For example, applied to the LB diagnostic method, the synergy concept significantly enhances the capabilities of the method, because the weighting mechanism of the LB method can be used to detect and account for the presence of synergy in the patient's reported symptoms. In fact, synergy allows the LB engine to dynamically adjust the very diagnostic process itself after every response from the patient. The synergy invention approximates the cognitive process of a human medical expert by providing for non-linear weighting of symptoms, by incrementally adding small weights to account for fine differences in patient health states, by fine-tuning the diagnosis, and by dynamically guiding the diagnostic process itself into productive channels. Using synergy, the symptom object of the LB method becomes a smart process that does not just store symptom values, but that can perform dynamic intelligent internal analysis of how the symptom behaved over time in the patient, which generates useful diagnostic information in its own right. In the context of certain embodiment of the LB diagnostic method, the word "synergy" has the normal dictionary meaning of "combined operation or action". Again, it refers to measuring the special, additional impact of several symptoms or symptom changes being present at the same time or in some prescribed sequence. The synergy concept implements in software the real-world medical fact that the diagnostic significance of a syndrome is much greater than the significance of its component symptoms in isolation. As detailed later for every individual synergy type, the synergy concept significantly enhances the LB method, so that special health conditions and their changes in a patient can be: detected by suitable questioning or computation, and assigned diagnostic weights in advance, then combined logically and mathematically, and used to score candidate diseases, which are used to rank candidate diseases, which are finally used to select those diseases that the patient most likely has. The system diagnostic methods include a novel and non-obvious way to compute a medical diagnosis. By this method, an medical script author can describe in advance certain specific health conditions and effects in a patient which tend to be less obvious and more difficult to detect by other methods. In certain embodiments of the present automated medical diagnosis system, synergy means special manifestations in the anatomic systems, over time, of patient-specific symptoms and patient-driven verbal descriptions of symptoms. The synergy invention mimics human cognition by providing for non-linear weighting of diagnoses by incrementally adding fine differences in health states, by fine-tuning the initial diagnoses, i.e., by allowing the medical judgments of the script authors to be implemented in an automated manner. Synergy allows the LB engine to watch and to dynamically adjust the very diagnostic process itself after every response from the patient. Recall that the LB method defines a "symptom" as any patient data item that can affect diagnosis. Therefore, all of the mechanisms used by the LB method to select, evaluate, and record the impact of symptoms are available and used to handle synergies. At script writing time, authors define synergies and assign weights to them like any other symptom. If the author intends to weight the, say, symmetry of onset and offset, the author defines a symptom and a question that will elicit the information directly from the patient or indirectly from other data such as the value of other symptoms. At run time, the LB engine, whether in the Horizontal Axis of Inquiry or in the Vertical Axis of Inquiry, selects symptoms, evaluates them, and--if applicable--adds the associated weight to them. One feature of the LB method that bears noting, is that it diagnoses disease by assigning "weights" to the patient's symptoms and then uses the weight accumulated by a set of candidate diseases to determine which disease(s) the patient most likely has. The basic weight assigned is for the mere presence or absence of a symptom. Now, under the synergy concept in certain embodiments, there are two additional ways to analyze more detailed aspects of symptoms. First, for each individual symptom, the system can diagnose based on whether it is "first" for a given disease, and on the manner in which the symptom starts, varies, and stops. Second, when several symptoms are present, the system can diagnose based on their presence as a combination, their sequence and extent of overlap in time, and their relationship to (and change in) the anatomic systems of the patient. In other words, these synergistic weights are "refinements" of the fundamental weighting. They specify in detail the considerations for which the LB method can add extra diagnostic weights to a disease. This idea is referred to as "synergy weighting"; it reflects the fact that more detailed knowledge about one or more symptoms of a patient can be used to refine and focus the diagnosis. The following table lists several type examples of synergy that can be implemented. The examples are, of course, not exhaustive; they can be extended to any special pattern of one or more symptoms occurring in a patient.
Synergy Type This synergy adds weight to a disease if it detects
that . . .
Symptom Presence patient has symptom A (the basic weighting con-
cept)
Symptom Level symptom A has specific value (PAIN SEVERITY =
8 out of 10)
Time-Based symptom A varies, cycles, pulsates, comes/goes, re-
Synergy peats
Onset/Offset symptom A starts or stops at a specified rate
Slope
Onset/Offset the rate of symptom A is changing in a specified
Trend way
Onset/Offset symptom A starts and stops in a similar manner
Symmetry
First Significant patient has the same FSS as defined for the disease
Symptom (FSS)
Simultaneous patient has a specified symptom set A, F, J, and R.
Sequencing symptoms A and B occur in a specified sequence
Overlap Synergy the length of time that symptoms A and B occur to-
gether
Integral Synergy the area under the curve of, for example, plotting
the severity of a patient's pain over time
The following sections relate to describing and weighting specific synergy types in certain embodiments of the invention. 1. Symptom Presence Synergy Symptom presence synergy assigns basic diagnostic weight to a candidate disease if a given symptom is present. At design time, the author can assign a weight to a symptom if it is present. For example, if the patient has a smoking history of ten pack-years, the disease EMPHYSEMA may get 50 points; if the patient recently went on a jungle expedition, the disease MALARIA may get 50 points. At run time, the system determines if the symptom is present in the patient and assigns a weight to all diseases for which the symptom has been pre-defined with a weight. Knowing the presence of symptoms, even without a value or time reference, can help to select candidate diseases for subsequent diagnosis. Thus, a different set of candidate diseases can be selected initially for a patient complaining of COUGH than for a patient complaining of BACKPAIN. 2. Symptom Level Synergy Symptom level synergy assigns diagnostic weight based on a level of a symptom present in a patient. At design time, the author defines several levels for a symptom, and weights for significant levels, such as:
SEVERITY = 0 0 points;
SEVERITY = 1 10 points;
.
.
.
SEVERITY = 9 250 points;
SEVERITY = 10 350 points.
At run time, the system determines: (1) if the symptom is present and (2) at what level and, if so, (3) adds the corresponding weight to the disease score. The author can define symptom magnitude with any appropriate resolution. This is obviously very useful in describing diseases more precisely in terms of their symptom patterns. 3. Time-Based Synergy Time-Based Synergy is the ability of the LB method to analyze the manner in which a symptom varies over time in the patient, and to assign extra diagnostic weight to selected diseases based thereon. The way in which a symptom varies over time has great diagnostic significance. One example is pain over time. The concept of using word spectra with a series of graded adjectives has also been introduced, so that the words selected by the patient indicate varying degrees of symptom intensity. This synergy type includes the general ability to use various aspects of a symptom time series to aid in, or refine a, diagnosis. As described earlier, the symptom and valuator objects can be programmed with functions that compute (or ask the patient for) various time-based statistics such as onset, offset, slope, trend, curvature, area, etc. At run time, when a script requires a time-based statistic for a given symptom, the symptom object invokes its valuator object to compute them. Such computed values then become separate symptom values that can be weighted and scored like any other symptom values. Using this synergy type, the symptom/valuator object becomes a smart process that does not just store symptom values, but that can perform dynamic, intelligent, internal analysis of how the symptom behaved over time in the patient, which generates useful diagnostic information in its own right. The script author can distinguish or differentiate among candidate diseases based on when a symptom occurs in the patient, or on how the symptom varies over time in the patient. The author can use the actual symptom time variations to assign extra diagnostic weights to a disease. One of the key features of the LB method is that it can use the time when symptoms occur and change to help diagnose the patient. This outperforms many other automated diagnostic methods. 4. Onset/Offset Analysis Synergy In one embodiment of the invention, onset/offset analysis synergy adds extra diagnostic weight to a disease if a given symptom exhibits onset and/or offset in a specific manner. The type of onset and offset of a symptom can convey great diagnostic information. The following description is for onset analysis synergy; a similar description applies to offset analysis synergy. At design time, the script author specifies for each disease: (1) that a given symptom's onset is to be synergy weighted, (2) the onset types that can be used to select added synergy weight, (3) the synergy weights to be added depending on the onset type. At run time, in the phase where the system is adding synergy weights to candidate diseases, the system: (1) detects that a given symptom's onset is to be synergy weighted, (2) obtains the actual onset type for the symptom, (3) compares the actual onset type to the pre-defined type, (4) selects the onset synergy weight that corresponds to the actual type, (5) adds the selected onset synergy weight to the disease score. Two examples of this synergy are as follows: 1) the sinusoidal relationship of severity of pain in colic, 2) the "stuttering" start of unstable angina. 5. Onset/Offset Slope Synergy In one embodiment of the invention, onset/offset slope synergy adds extra diagnostic weight to a disease if a given symptom begins and rises to a maximum in a defined manner. The following description is for onset synergy; a similar description applies to the manner in which a symptom ends, or its offset. At design time, the script author specifies for each disease: (1) that a given symptom's onset is to be synergy weighted, (2) the onset slope threshold(s) to be used for selecting the synergy weight, (3) the synergy weights to be added depending on the magnitude of the onset slope. At run time, in the phase where the system is adding synergy weights to candidate diseases, the system: (1) detects that a given symptom's onset is to be synergy weighted, (2) obtains the actual onset slope for the symptom, (3) compares the actual onset slope to the pre-defined slope threshold(s), (4) selects the onset synergy weight that corresponds to the actual slope, (5) adds the selected onset synergy weight to the disease score. The nature of the onset (and offset) of a symptom can convey great diagnostic information. For example, a headache that starts suddenly and is very severe has more chance of being a sub-arachnoid hemorrhage than a severe headache that comes on gradually. In vascular events such as a myocardial infarction, the onset of pain is very sudden, that is, the slope of a line plotting severity versus time will be nearly vertical. The sudden onset of vomiting and diarrhea in Staph food poisoning contrasts to other causes of gastroenteritis and food poisoning. 6. Onset/Offset Trend Synergy In one embodiment of the invention, the onset (or offset) "trend" of a symptom refers to whether the symptom curve at that time point is linear or exponential, i.e., rising (or falling) at a constant rate or at an increasing or decreasing rate. This is referred to as "linear or exponential". The following description is for onset trend synergy; a similar description applies to the manner in which a symptom ends, or its offset. At design time, the author specifies for each disease: (1) that a given symptom's onset's curve trend is to be synergy weighted, (2) the onset trend threshold(s) to be used for selecting the synergy weight, (3) the synergy weights to be added depending on the trend of the onset slope. At run time, in the phase where the system is adding synergy weights to candidate diseases, the system: (1) detects that a given symptom's onset trend is to be synergy weighted, (2) obtains the actual onset trend for the symptom, (3) compares the actual onset trend to the pre-defined trend threshold(s), (4) selects the onset synergy weight that corresponds to the actual trend, (5) adds the selected onset synergy weight to the disease score. The shape of the onset (and offset) curve of a symptom can convey diagnostic information. In one embodiment, the diagnostic system uses a Runge-Kutta curve-fitting algorithm to identify the type of curve under consideration. Other algorithms are used in other embodiments. 7. Onset/Offset Symmetry Synergy In one embodiment of the invention, onset/offset symmetry synergy assigns extra diagnostic weight if the onset and offset curves (or slope if the relationship is linear) of a given symptom exhibit defined symmetry characteristics. At design time, the script author specifies for each disease: (1) that a given symptom's onset and offsets are to be weighted for symmetry, (2) the parameters that define various symmetry relationships, (3) the synergy weights to be added for a given symmetry relationship. At run time, in the phase where the system is adding synergy weights to candidate diseases, the system: (1) detects that a given symptom's onset/offset symmetry is to be synergy weighted, (2) obtains the actual onset and offset slopes and trends for the symptom, (3) converts the actual slopes and trends into pre-defined symmetry parameters, (4) selects the symmetry synergy weight that corresponds to the actual data, (5) adds the selected weight to the disease score. Onset and offset symmetry is important in making several diagnoses. For example, when a patient has a kidney stone that passes into the ureter (the tube connecting the kidney to the bladder), the patient experiences the sudden onset of very severe (and colicky) pain. In addition, when the stone passes into the bladder, the pain symptom frequently disappears as suddenly as it came on. 8. First Significant Symptom (FSS) Synergy In one embodiment of the invention, first significant symptom (FSS) synergy assigns extra diagnostic weight to a disease if the patient's FSS matches a list of possible FSSs for the disease. This synergy reflects the script author's real-world experience as to which symptoms tend to be the first ones noticed by a patient. At scripting time, a disease script author creates a special list of symptoms that a patient might notice first, and associates a weight with each symptom. For example, for Appendicitis:
Anorexia 50
Nausea 30
Epigastric Pain 10
At run time, if the patient reports that Nausea was the first symptom she or he noticed, the system will add 30 diagnostic points to the diagnosis of Appendicitis (and similarly add some weight to all other diseases that show Nausea in their FSS tables). The key is that we use the information that the patient has a specific symptom first to advance those diagnoses that match the patient. We already added points to the disease for just having this symptom; now we add extra weight for it being first. That's the meaning of FSS synergy. 9. Simultaneous Synergy In one embodiment of the invention, simultaneous synergy assigns extra diagnostic weight to a candidate disease if two or more of its symptoms are present in the patient over a given time period. At design time, for each disease, the script author can define any number of special symptom combinations as well as an associated diagnostic weight that should be added to the disease score if the combination is present. The disease author may use a Gantt chart to appreciate the simultaneous, sequential and overlapping synergies. At run time, for each disease, the system: (1) tracks the symptoms actually present in the patient, (2) determines if any pre-defined symptom combination is present, and--if so-- (3) adds the associated weight to the score of the disease. Simultaneous Synergy can be used very effectively by a script author to describe a disease in terms of syndrome, and to characterize how various syndromes contribute to the disease. 10. Sequencing Synergy In one embodiment of the invention, sequencing synergy assigns extra diagnostic weight to a candidate disease if two or more of its symptoms are present in the patient in a specific time sequence. At design time, for each disease, the Script author may define any number of special symptom sequences, with associated diagnostic weights to be added to the disease score if the patient exhibits the symptoms in the specified order. At run time, for each disease, the system: (1) establishes an absolute start time for every symptom, (2) tracks the symptoms actually present in the patient, (3) detects if any author-defined sequence is present, (4) determines whether the symptoms are present in the pre-defined time order, (5) if appropriate, adds the sequence weight to the score of the disease. 11. Overlapping Synergy In one embodiment of the invention, overlapping synergy assigns extra diagnostic weight to a candidate disease if two or more of its symptoms are present in the patient at the same time for a specified amount of time. At design time, for each disease, the author can define any number of special overlap symptom combinations, an overlap threshold, and a diagnostic weight that should be added to the disease score if the symptom combinations overlapped in time for at least the specified threshold time. At run time, for each disease, the system: (1) tracks the symptoms actually present in the patient, (2) detects if any author-defined overlapping symptoms are present, (3) computes for how long the symptoms overlapped in time, (4) checks if the actual overlap meets or exceeds the specified overlap threshold, (5) if applicable, adds the specified overlap weight to the score of the disease. 12. Integral (Area) Synergy In one embodiment of the invention, integral synergy assigns extra diagnostic weight for the total amount of a symptom over a specified time period. At design time, the author assigns diagnostic weight for the amount of a symptom that a patient has reported over a time interval. At run time, the system (1) tracks the time chart of the symptom values, (2) computes the total symptom value (i.e., integrates the symptom curve) between two time points, and (3) if appropriate, adds an area synergy weight to the disease score. The amount of a symptom over time gives information regarding biological and chemical body functions and reactions, which, in turn, have diagnostic value. An example is the amount of pain a patient has suffered between two points in time. The integral synergy weight helps the system automatically recognize those patients that can benefit from strong analgesics, for example. In addition to diagnosis, this synergy could also be used for pain management. It could identify those patients who perhaps cannot, or do not, identify themselves as needing narcotic analgesics. V. Descriptions of the Drawings The software described by the following drawings is executed on a structure-based engine of a medical diagnostic and treatment advice system such as described in Applicant's U.S. Pat. No. 5,935,060. One embodiment of a structure-based engine is the list-based engine, but other embodiments may be implemented. Referring now to FIG. 1, one embodiment of a diagnostic loop portion 100 of a medical diagnostic and treatment advice (MDATA) system, that may include a List-Based Engine (LBE), will be described in terms of its major processing functions. Note that treatment advice may be optionally provided. However, the diagnostic aspect of this system is the main focus of the invention. Each function is further described with an associated figure. When the system starts, it assumes that another, off-line data preparation program has prepared a suitable database of medical diagnostic data in the form of disease and symptom objects, DOs and SOs respectively, and has assigned diagnostic weights to specific symptom values for each disease, and to special combinations or sequences of symptom values (called "synergies"). When a patient (who may access the system via a data communications network such as the Internet) presents a medical complaint to the MDATA system to be diagnosed, the system first retrieves all of the relevant disease objects from its database and assembles them into a candidate disease list. The system then uses the diagnostic loop to develop a diagnostic profile of the candidate disease list. Inside the diagnostic loop, the system selects a current disease and symptom to pursue. Then the system obtains the value of the symptom for the current patient, calculates the weights associated with that value, and updates the scores of all affected candidate diseases with the weights. The updated scores are then used to re-rank the diseases and to select the disease and symptom to be evaluated in the next iteration. In this manner, as the loop continues to iterate, the system builds up a diagnostic profile of the candidate diseases for the current patient. The loop can be interrupted at any point, and the then-current diagnostic profile examined in order to adjust the system parameters and continue the loop, or to terminate the loop, as desired. At the end of the loop, a diagnostic report is prepared that summarizes the actions taken and the results computed. The diagnostic loop 100 begins at state 102, where prior processing is assumed to have established a chief complaint for the current patient and a diagnostic report needs to be determined. Moving to function 110, the system acquires the computer resources required for the diagnostic loop. In this function, the system acquires computer memory as needed, creates required software objects, and sets variables to their initial values based on the current options, limits, and diagnostic goals. The system also creates a list of diseases that are to be used as the initial candidates for diagnosis. Moving to function 120, the system selects one disease from the list of candidate diseases. This disease becomes the current focus disease, i.e., the disease for which a symptom is to be evaluated. Moving to function 130, the system selects one symptom from the list of symptoms associated with the current disease. This symptom becomes the current focus symptom to be evaluated in the patient. Moving to function 140, the system evaluates the current symptom in the patient by suitable means such as questioning the patient, using logical inferences, mathematical computations, table lookups, or statistical analysis involving other symptom values. Moving to function 150, the system updates all candidate diseases that use the current symptom with the new symptom value obtained in function 140. Moving to function 160, the system updates all working lists and records with new values, scores, and diagnoses. Moving to function 170, the system reviews the progress of the diagnosis to decide whether another iteration of the diagnostic loop is required. Proceeding to a decision state 172, the system tests whether the diagnostic loop is to be terminated e.g., by user direction. If not, the system moves to function 120 for another iteration; otherwise, the system moves to function 180, where the system saves appropriate values computed in the diagnostic loop and destroys all temporary data structures and objects required for the diagnostic loop. Continuing at state 182, the system returns a report of the diagnostic results. Referring now to FIG. 2, the Set-up Diagnostic Loop function 110 previously shown in FIG. 1 will be described. Function 110 acquires the computer resources and sets up the data structures needed for the diagnostic loop 100 (FIG. 1). The system is designed to be fully adaptive to its environment, and must initialize various memory structures to prepare for processing. In an object-based embodiment, this preparation includes the creation of various objects. Each object has an initialization function that allows the object to initialize itself as needed. Function 110 begins with entry state 202, where a chief complaint and a diagnostic mode have been established by prior processing. The chief complaint will be used in state 212 to retrieve associated diseases. The diagnostic mode will be used throughout function 110 to control detail processing. Moving to state 204, function 110 initializes the HAI/VAI mode to either HAI or VAI, depending on the HAI/VAI mode desired for this operation of the diagnostic loop. In HAI mode, the system will consider all of the candidate diseases to select the next focus symptom; in VAI mode, the system will use only the list of symptoms of the current focus disease. Moving to state 206, function 110 initializes the Alternative Symptom mode to either permit or inhibit alternative symptom valuation, depending on the Alternative Symptom mode desired for this operation of the diagnostic loop. If alternative symptoms are permitted, the system will later accept alternative values in place of the specified symptom value. If alternative symptoms are inhibited, the system will later insist on evaluating the specified symptom. Moving to state 208, function 110 initializes other internal variables that support the loop processing such as control flags, option indicators, loop limits, and loop goals. The exact variable and value of each variable depends on the particular code embodiment chosen for the computer program. Moving to state 210, function 110 obtains and initializes special computer resources required for this operation of the diagnostic loop. The details of this initialization depend on the code embodiment chosen for the computer program. For example, if an object is used to represent the list of candidate diseases, state 210 creates and initializes an empty candidate disease object; but if a relational table is used to represent the list of candidate diseases, state 210 creates and initializes an empty table to contain the candidate diseases. Moving to state 212, function 110 retrieves from the disease database all those diseases that exhibit the chief complaint being diagnosed and (if available) the patient's symptom time profile (FIG. 28). As part of the off-line data gathering and preparation process, every disease in the disease object database is associated with at least one chief complaint and with a time profile of symptoms. This association is used here to retrieve the list of diseases that constitute the initial candidate set. By "candidate" disease is meant any human disease, as yet unidentified, that has some probability of being the patient's illness, based on the symptoms and the chief complaint indicated by the patient. Moving to a function 220, various internal working structures required to efficiently perform such detailed processing as sorting, searching, and selecting subsets of diseases and symptoms are initialized. Function 220 is further described in conjunction with FIG. 3. Moving to state 222, function 110 returns control to the process that called it, in effect moving to function 120 of FIG. 1. Referring now to FIG. 3, the Set-up Disease-Symptom Structure function 220, which sets up the candidate disease list and actual symptom data structures to be used in the diagnostic loop, will be described. The candidate disease list is generated and then partitioned into three sublists of urgent, serious, and common diseases. This separation allows the system to consider | ||||||