Integrating Learning from Examples into the Search for Diagnostic Policies

TL;DR

This paper presents a formal framework and new algorithms for learning cost-effective diagnostic policies from data, balancing test costs and misdiagnosis costs, and demonstrates their effectiveness on benchmark datasets.

Contribution

It introduces a Markov Decision Process formulation for diagnostic decision making and develops efficient systematic search algorithms with heuristics and regularization to learn policies from examples.

Findings

Systematic search algorithms outperform greedy methods in most cases.

The proposed algorithms are practical on standard desktop computers.

Regularization reduces overfitting in learned diagnostic policies.

Abstract

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.