Clustering longitudinal clinical marker trajectories from electronic health data: Applications to phenotyping and endotype discovery

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Diseases such as autism, cardiovascular disease, and the autoimmune disorders are difficult to treat because of the remarkable degree of variation among affected individuals. Subtyping research seeks to refine the definition of such complex, multi-organ diseases by identifying homogeneous patient subgroups. In this paper, we propose the Probabilistic Subtyping Model (PSM) to identify subgroups based on clustering individual clinical severity markers. This task is challenging due to the presence of nuisance variability-variations in measurements that are not due to disease subtype-which, if not accounted for, generate biased estimates for the group-level trajectories. Measurement sparsity and irregular sampling patterns pose additional challenges in clustering such data. PSM uses a hierarchical model to account for these different sources of variability. Our experiments demonstrate that by accounting for nuisance variability, PSM is able to more accurately model the marker data. We also discuss novel subtypes discovered using PSM and the resulting clinical hypotheses that are now the subject of follow up clinical experiments.

Original languageEnglish (US)
Title of host publicationProceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
PublisherAI Access Foundation
Pages2956-2964
Number of pages9
ISBN (Electronic)9781577357025
StatePublished - Jun 1 2015
Event29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 - Austin, United States
Duration: Jan 25 2015Jan 30 2015

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume4

Other

Other29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
CountryUnited States
CityAustin
Period1/25/151/30/15

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Schulam, P., Wigley, F., & Saria, S. (2015). Clustering longitudinal clinical marker trajectories from electronic health data: Applications to phenotyping and endotype discovery. In Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 (pp. 2956-2964). (Proceedings of the National Conference on Artificial Intelligence; Vol. 4). AI Access Foundation.