Integrating Learning from Examples into the Search for Diagnostic Policies

This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decision-making actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected total cost, which is the sum of measurement costs and misdiagnosis costs. In most diagnostic settings, there is a tradeoff between these two kinds of costs. This paper formalizes diagnostic decision making as a Markov Decision Process (MDP). The paper introduces a new family of systematic search algorithms based on the AO* algorithm to solve this MDP. To make AO* efficient, the paper describes an admissible heuristic that enables AO* to prune large parts of the search space. The paper also introduces several greedy algorithms including some improvements over previously-published methods. The paper then addresses the question of learning diagnostic policies from examples. When the probabilities of diseases and test results are computed from training data, there is a great danger of overfitting. To reduce overfitting, 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 produce better diagnostic policies than the greedy methods. In addition, the studies show that for training sets of realistic size, the systematic search algorithms are practical on todays desktop computers

Integrating learning from examples into the search for diagnostic policies

This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decision-making actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected total cost of diagnosing a patient, where the cost is the sum of two components: (a) measurement costs (the costs of performing various diagnostic tests) and (b) misdiagnosis costs (the costs incurred when the patient is incorrectly diagnosed). In most diagnostic settings, there is a tradeoff between these two kinds of costs. A diagnostic policy that minimizes measurement costs usually performs fewer tests and tends to make more diagnostic errors, which are expensive. Conversely, a policy that minimizes misdiagnosis costs usually makes more measurements. This paper formalizes diagnostic decision making as a Markov Decision Process (MDP). It then presents a range of algorithms for solving this MDP. These algorithms can be divided into methods based on systematic search and methods based on greedy search. The paper introduces a new family of systematic algorithms based on the AO* algorithm. To make AO* efficient, the paper describes an admissible heuristic that enables AO* to prune large parts of the search space. The paper also introduces several greedy algorithms including some improvements over previously-published methods. The paper then addresses the question of learning diagnostic policies from examples. When the probabilities of diseases and test results are computed from training data, there is a great danger of overfitting. The paper introduces a range of regularization methods to reduce overfitting. An interesting aspect of these regularizers is that they are integrated into the search algorithms rather than being isolated in a separate learning step prior to searching for a good diagnostic policy. Finally, the paper compares the proposed methods on five benchmark diagnostic data sets. The studies show that in most cases the systematic search methods produce better diagnostic policies than the greedy methods. In addition, the studies show that for training sets of realistic size, the systematic search algorithms are practical on today's desktop computers. Hence, these AO*-based methods are recommended for learning diagnostic policies that seek to minimize the expected total cost of diagnosis.Keywords: diagnostic policy, AO*, Markov decision process, diagnostic decision makin

Integrating learning from examples into the search for diagnostic policies

This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decision-making actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected total cost, which isthe sum of measurement costs and misdiagnosis costs. In most diagnostic settings, there is a tradeo between these two kinds of costs. This paper formalizes diagnostic decision making as a Markov Decision Process (MDP). The paper introduces a new family of systematic search algorithms based on the AO algorithm to solve this MDP.To makeAO e cient, the paper describes an admissible heuristic that enables AO to prune large parts of the search space. The paper also introduces several greedy algorithms including some improvements over previously-published methods. The paper then addresses the question of learning diagnostic policies from examples. When the probabilities of diseases and test results are computed from training data, there is a great danger of over tting. To reduce over tting, regularizers are integrated into the search algorithms. Finally, the paper compares the proposed methods on ve benchmark diagnostic data sets. The studies show that in most cases the systematic search methods produce better diagnostic policies than the greedy methods. In addition, the studies show that for training sets of realistic size, the systematic search algorithms are practical on today's desktop computers. 1

Integrating learning from examples into the search for diagnostic policies

This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decision-making actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected total cost, which is the sum of measurement costs and misdiagnosis costs. In most diagnostic settings, there is a tradeoff between these two kinds of costs. This paper formalizes diagnostic decision making as a Markov Decision Process (MDP). The paper introduces a new family of systematic search algorithms based on the AO ∗ algorithm to solve this MDP. To make AO ∗ efficient, the paper describes an admissible heuristic that enables AO ∗ to prune large parts of the search space. The paper also introduces several greedy algorithms including some improvements over previously-published methods. The paper then addresses the question of learning diagnostic policies from examples. When the probabilities of diseases and test results are computed from training data, there is a great danger of overfitting. To reduce overfitting, 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 produce better diagnostic policies than the greedy methods. In addition, the studies show that for training sets of realistic size, the systematic search algorithms are practical on today’s desktop computers. 1

Learning Diagnostic Policies from Examples Integrating Learning from Examples into the Search for Diagnostic Policies ∗

This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decision-making actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected total cost of diagnosing a patient, where the cost is the sum of two components: (a) measurement costs (the costs of performing various diagnostic tests) and (b) misdiagnosis costs (the costs incurred when the patient is incorrectly diagnosed). In most diagnostic settings, there is a tradeoff between these two kinds of costs. A diagnostic policy that minimizes measurement costs usually performs fewer tests and tends to make more diagnostic errors, which are expensive. Conversely, a policy that minimizes misdiagnosis costs usually makes more measurements. This paper formalizes diagnostic decision making as a Markov Decision Process (MDP). It then presents a range of algorithms for solving this MDP. These algorithms can be divided into methods based on systematic search and methods based on greedy search. The paper introduces a new family of systematic algorithms based on the AO ∗ algorithm. To mak