Clinical Trajectory Representations for Clustering
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Author / Producer
Date
2023
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Analyzing and grouping typical patient trajectories is crucial to understanding their health state, estimating prognosis, and determining optimal treatment. The increasing availability of electronic health records (EHRs) opens the opportunity to support clinicians in their decisions with machine learning solutions. We propose the Multi-scale Health-state Variational Auto-Encoder (MHealthVAE) to learn medically informative patient representations and allow meaningful subgroup detection from sparse EHRs. We derive a novel training objective to better capture health information and temporal trends into patient embeddings and introduce new performance metrics to evaluate the clinical relevance of patient clustering results.
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published
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Book title
ICLR 2023 Workshop on Time Series Representation Learning for Health
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Publisher
OpenReview
Event
Workshop on Time Series Representation Learning for Health (TSRL4H @ ICLR 2023)
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09568 - Rätsch, Gunnar / Rätsch, Gunnar
02219 - ETH AI Center / ETH AI Center