Learning Diagnostic Policies from Examples by Systematic Search
This work addresses the challenge of efficient and accurate diagnosis in domains like healthcare or engineering, but it is incremental as it builds on existing MDP and search techniques with a focus on overfitting prevention.
The paper tackles the problem of learning cost-sensitive diagnostic policies from examples by formalizing diagnosis as a Markov Decision Process and using systematic search algorithms like AO* to find optimal policies that minimize expected total costs. It shows experimentally that systematic search methods outperform greedy methods on benchmark datasets, though specific numerical gains are not provided.
A diagnostic policy specifies what test to perform next, based on the results of previous tests, and when to stop and make a diagnosis. Cost-sensitive diagnostic policies perform tradeoffs between (a) the cost of tests and (b) the cost of misdiagnoses. An optimal diagnostic policy minimizes the expected total cost. We formalize this diagnosis process as a Markov Decision Process (MDP). We investigate two types of algorithms for solving this MDP: systematic search based on AO* algorithm and greedy search (particularly the Value of Information method). We investigate the issue of learning the MDP probabilities from examples, but only as they are relevant to the search for good policies. We do not learn nor assume a Bayesian network for the diagnosis process. Regularizers are developed to control overfitting and speed up the search. This research is the first that integrates overfitting prevention into systematic search. The paper has two contributions: it discusses the factors that make systematic search feasible for diagnosis, and it shows experimentally, on benchmark data sets, that systematic search methods produce better diagnostic policies than greedy methods.