@inproceedings{92c9561e8592440c94b977d46c87312d,
title = "Outcomes-Driven Clinical Phenotyping in Cardiogenic Shock using a Mixture of Experts",
abstract = "Cardiogenic shock (CS) is a deadly and complicated illness. Despite extensive research into its treatment, mortality remains high and has not decreased over time. Patients suffering from CS are highly heterogeneous. Developing an understanding of phenotypes among these patients is crucial for understanding this disease and appropriate treatments for individual patients. In this work, we develop a deep mixture of experts approach to jointly find phenotypes among patients with CS while simultaneously estimating their risk of in-hospital mortality. This model is applied to a cohort of 28,304 patients with CS, predicting in-hospital mortality with an AUROC of 0.85 ± 0.01 and discovering five phenotypes among the population. This approach allows for grouping patients in clinical clusters with different rates of device utilization and different risk of mortality. This approach jointly finds phenotypes within a clinical population and in modeling risk among that population.",
keywords = "Cardiology, Knowledge engineering, Machine learning, Medical information systems, Neural networks",
author = "Hurley, {Nathan C.} and Alyssa Berkowitz and Frederick Masoudi and Joseph Ross and Nihar Desai and Nilay Shah and Sanket Dhruva and Mortazavi, {Bobak J.}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2021 ; Conference date: 27-07-2021 Through 30-07-2021",
year = "2021",
doi = "10.1109/BHI50953.2021.9508568",
language = "English (US)",
series = "BHI 2021 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "BHI 2021 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings",
}