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Date
2023Type
- Conference Paper
ETH Bibliography
yes
Altmetrics
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. Show more
Publication status
publishedBook title
ICLR 2023 Workshop on Time Series Representation Learning for HealthPublisher
OpenReviewEvent
Organisational unit
02219 - ETH AI Center / ETH AI Center09568 - Rätsch, Gunnar / Rätsch, Gunnar
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ETH Bibliography
yes
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