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Research questionHow can fragmented longitudinal oncology records be converted into accurate, source-attributed structured data?Oncology information is distributed across reports and encounters, making manual abstraction time-consuming and prone to losing the source or context of each value. The challenge is to structure this information without conflating findings from different specimens, tumors, biomarkers, or dates.
AI
Health
Information Retrieval
Multi-agent Systems
Natural Language Processing
Latest papersRecent research connected to this question, newest first.From Analytics to Tumor Boards: An Evidence-Linked Multi-Agent Workflow for Oncology Feature ExtractionThe evidence concerns an oncology information-extraction workflow applied to integrated EHR documentation. It covers 328 clinician-defined attributes across report metadata, diagnosis, staging, and cancer-specific information, evaluated retrospectively on 230 de-identified documents from 40 patients. Review covered selected fields identified as present in the source documents, comprising 418 document-field pairs and 1,126 non-empty reference values; it did not exhaustively annotate all schema fields.research paper · Sep 2, 2026
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