Visualizing Clinical Significance with Prediction and Tolerance Regions


Maria Jahja, Daniel J. Lizotte ;
Proceedings of the 2nd Machine Learning for Healthcare Conference, PMLR 68:217-230, 2017.


The goal of this work is to better convey the evidence for or against clinically significant differences in patient outcomes induced by different treatment policies. In pursuit of this goal, we present a framework for computing and presenting prediction regions and tolerance regions for the outcomes of a treatment policy operating within a multi-objective Markov decision process (MOMDP). Our framework draws on two bodies of existing work, one in computer science for learning in MOMDPs, and one in statistics for uncertainty quantification. We review the relevant methods from each body of work, present our framework, and illustrate its use using data from the Clinical Antipsychotic Trials of Intervention Effectiveness (Schizophrenia). Finally, we discuss potential future directions of this work for supporting sequential decision-making.

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