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Clinical Tagging with Joint Probabilistic Models

Yoni Halpern, Steven Horng, David Sontag
Proceedings of the 1st Machine Learning for Healthcare Conference, pp. 209–225, 2016

Abstract

We describe a method for parameter estimation in bipartite probabilistic graphical models for joint prediction of clinical conditions from the electronic medical record. The method does not rely on the availability of gold-standard labels, but rather uses noisy labels, called anchors, for learning. We provide a likelihood-based objective and a moments-based initialization that are effective at learning the model parameters. The learned model is evaluated in a task of assigning a heldout clinical condition to patients based on retrospective analysis of the records, and outperforms baselines which do not account for the noisiness in the labels or do not model the conditions jointly.

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