Home Page

Papers

Submissions

News

Editorial Board

Announcements

Proceedings

Open Source Software

Search

Login



RSS Feed

Directly Modeling Missing Data in Sequences with RNNs: Improved Classification of Clinical Time Series

Zachary C Lipton, David Kale, Randall Wetzel
Proceedings of the 1st Machine Learning for Healthcare Conference, pp. 253–270, 2016

Abstract

We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected from the intensive care unit (ICU) of a major urban medical center, our data consists of multivariate time series of observations. The data is irregularly sampled, leading to missingness patterns in re-sampled sequences. In this work, we show the remarkable ability of RNNs to make effective use of binary indicators to directly model missing data, improving AUC and F1significantly. However, while RNNs can learn arbitrary functions of the missing data and observations, linear models can only learn substitution values. For linear models and MLPs, we show an alternative strategy to capture this signal. Additionally, we evaluate LSTMs, MLPs, and linear models trained on missingness patterns only, showing that for several diseases, what tests are run can be more predictive than the results themselves.

Related Material