A comparison of methods for diagnostic decision making

摘要

Diagnosis is the process of determining the correct problem from a collection of problems given a set of symptoms that indicate a problem exists. Common experiences with this process include visits to the physician in order to determine our illness (disease) and visits to our local mechanic to determine the cause (fault) of a poorly operating car. In either case, we report the symptoms of the problem to the diagnostician (physician or mechanic) who determines the most likely cause that best explains these symptoms. In terms of the complexity of finding the correct problem, the diagnostician must fund a diagnosis from a set of possible diagnoses. That is, if a total of 10 problems are being considered where only one of these is correct then at most 10 diagnoses will need to be evaluated.However, in the more typical case where several problems (diseases/faults) may simulataneously, the complexity of finding a proper diagnosis increases exponentially with the number of problems. For example, using the 10 problems considered above, the situation changes to where any of the 1024 possible combinations of problems may turn out to be the correct diagnosis. In this paper, we compare several automated methods for diagnosing multiple simultaneous problems. These methods range from exhaustive (testing every possible combination and selecting the most likely) to heuristic (testing only a small percentage of the total combinations, yet finding a satisfactory diagnosis). Advantages and disadvantages of each method are detailed along with a comparison of their respective runtimes and reliability.