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This paper studies the problem of learning diagnostic policies from trainingexamples. A diagnostic policy is a complete description of the decision-makingactions of a diagnostician (i.e., tests followed by a diagnostic decision) forall possible combinations of test results. An optimal diagnostic policy is onethat minimizes the expected total cost, which is the sum of measurement costsand misdiagnosis costs. In most diagnostic settings, there is a tradeoffbetween these two kinds of costs. This paper formalizes diagnostic decisionmaking as a Markov Decision Process (MDP). The paper introduces a new family ofsystematic search algorithms based on the AO* algorithm to solve this MDP. Tomake AO* efficient, the paper describes an admissible heuristic that enablesAO* to prune large parts of the search space. The paper also introduces severalgreedy algorithms including some improvements over previously-publishedmethods. The paper then addresses the question of learning diagnostic policiesfrom examples. When the probabilities of diseases and test results are computedfrom training data, there is a great danger of overfitting. To reduceoverfitting, regularizers are integrated into the search algorithms. Finally,the paper compares the proposed methods on five benchmark diagnostic data sets.The studies show that in most cases the systematic search methods producebetter diagnostic policies than the greedy methods. In addition, the studiesshow that for training sets of realistic size, the systematic search algorithmsare practical on todays desktop computers.

Integrating Learning from Examples into the Search for Diagnostic Policies