Overview

This application of SRI's evidential reasoning technology highlights its ability to simultaneously reason from any subset of some 60 different inputs, of varying reliability and relevance, to automatically diagnose a patient complaining of chest pains.

Domain

When a patient complaining of chest pains reports to a physician, a large number of possibly relevant inputs must be considered during the diagnosis. Some of these inputs are subjective, being observations made by the attending physician (e.g., What is the patient's skin color?) or the patient's answers to the physician's questions (e.g., Where does the pain originate?). Other inputs are the objective results of laboratory tests or measurements (e.g., What is the patient's temperature?). We have identified some 56 different inputs that are relevant to selecting among 12 possible pathologies. The relative diagnostic significance of these inputs depends on both the degree of certainty with which the inputs are known (if at all) and their native strength as discriminators among the pathologies under consideration.

Application

The first step in constructing this application was to conduct a literature search for studies that reported on the diagnostic relevance of various indicators for those pathologies associated with chest pains. For some indicators, the appropriate clinical data was available to statistically estimate conditional probabilities; for others, expert estimates of these conditionals had to suffice; for still others, no reasonable basis for estimating the conditionals was available, but the presence or absence of the indicator was known to logically eliminate some pathologies. In all cases, probability bounds were substituted for point probabilities where they were not precisely known.

The next step was to establish Gister's gallery that delimits the possible pathologies and indicator values. It consists of a frame of discernment for the pathologies (lower left) and one for each indicator, and a compatibility relation connecting each indicator frame to the pathology frame. These relations capture which pathologies are consistent with each indicator value.

Finally, we established a Gister analysis (lower right) that records the probabilistic relationships, delimited in the gallery, and the patient-specific data recorded by the attending physician. Once recorded, the analysis functions as a dataflow model of the diagnostic procedure; as data progresses through the analysis, evidential reasoning operations calculate the relevance of each of the given indicators and fuse them to arrive at a consensus based on all of the data or restricted to particular subsets of data (e.g., pertaining to the description of the pain). While entering the available data, the physician also enters a credibility capturing the physician's faith in the response. As more credible data is entered, the diagnosis becomes more specific.

Performance

This application of evidential reasoning highlights its ability to reason with data of varying reliability. Both subjective and objective observations are combined with clinically validated data, expert opinion, and logical possibilities. Each patient workup can be run even if significant portions of the patient's history and physical exam are missing. The physician fills out a multiple-choice questionnaire (upper left), provides a credibility rating for each answer, and the system determines the diagnosis. The physician can examine different places in the analysis to see how various portions of the patient's workup may suggest different diagnoses.This system has already been shown to provide accurate diagnoses when run on actual patient cases with problems ranging from acute myocardial infarction to anxiety and esophagitis.

John D. Lowrance, <lowrance@ai.sri.com>
Artificial Intelligence Center
SRI International

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