AI RESEARCH
Early Risk Prediction with Temporally and Contextually Grounded Clinical Language Processing
arXiv CS.CL
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ArXi:2511.22038v2 Announce Type: replace Clinical notes in Electronic Health Records (EHRs) capture rich temporal information on events, clinician reasoning, and lifestyle factors often missing from structured data. Leveraging them for predictive modeling can be impactful for timely identification of chronic diseases. However, they present core natural language processing (NLP) challenges: long text, irregular event distribution, complex temporal dependencies, privacy constraints, and resource limitations.