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Sequential Inference of Hospitalization ElectronicHealth Records Using Probabilistic Models
March 29, 2024, 4:42 a.m. | Alan D. Kaplan, Priyadip Ray, John D. Greene, Vincent X. Liu
cs.LG updates on arXiv.org arxiv.org
Abstract: In the dynamic hospital setting, decision support can be a valuable tool for improving patient outcomes. Data-driven inference of future outcomes is challenging in this dynamic setting, where long sequences such as laboratory tests and medications are updated frequently. This is due in part to heterogeneity of data types and mixed-sequence types contained in variable length sequences. In this work we design a probabilistic unsupervised model for multiple arbitrary-length sequences contained in hospitalization Electronic Health …
abstract arxiv cs.lg data data-driven decision decision support dynamic future hospital improving inference laboratory part patient q-bio.qm records support tests tool type
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