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Autoregressive Language Models For Estimating the Entropy of Epic EHR Audit Logs

arXiv 2311.06401 ↗First posted 10 Nov 2023cs.CL cs.IT math.ITcited by 0

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Abstract (arXiv)

EHR audit logs are a highly granular stream of events that capture clinician activities, and is a significant area of interest for research in characterizing clinician workflow on the electronic health record (EHR). Existing techniques to measure the complexity of workflow through EHR audit logs (audit logs) involve time- or frequency-based cross-sectional aggregations that are unable to capture the full complexity of a EHR session. We briefly evaluate the usage of transformer-based tabular language model (tabular LM) in measuring the entropy or disorderedness of action sequences within workflow and release the evaluated models publicly.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 22 references that are in the map. First posted on arXiv 10 Nov 2023.

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References · 11

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