Clustering Interval-Censored Time-Series for Disease Phenotyping

Abstract

Unsupervised learning is often used to uncover clusters in data. However, different kinds of noise may impede the discovery of useful patterns from real-world time-series data. In this work, we focus on mitigating the interference of interval censoring in the task of clustering for disease phenotyping. We develop a deep generative, continuous-time model of time-series data that clusters time-series while correcting for censorship time. We provide conditions under which clusters and the amount of delayed entry may be identified from data under a noiseless model. On synthetic data, we demonstrate accurate, stable, and interpretable results that outperform several benchmarks. On real-world clinical datasets of heart failure and Parkinson’s disease patients, we study how interval censoring can adversely affect the task of disease phenotyping. Our model corrects for this source of error and recovers known clinical subtypes.

Publication
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI)
Irene Chen
Irene Chen
PhD Student

Irene works on machine learning methods to improve understanding of human health and reduce inequality.

Rahul Krishnan
Rahul Krishnan
PhD Student

Assistant Professor, University of Toronto

David Sontag
David Sontag
Associate Professor of EECS

My research focuses on advancing machine learning and artificial intelligence, and using these to transform health care.

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