aceAI-Powered Structuring of Clinical Information in Parkinson's Disease: An Approach Based on Large Language Models
Advanced Parkinson's Disease (APD) is clinically heterogeneous, and many APD-defining features are recorded predominantly in unstructured neurology narratives, limiting scalable phenotyping and longitudinal analyses. An approach based on large language models (LLMs) was designed to transform free text from electronic health records (EHRs) into an APD-oriented dataset aligned with Questionnaire for the Evaluation of Advanced Parkinson's Disease (CDEPA)-derived domains. A retrospective EHR cohort identified betwee…
A schema-guided LLM pipeline with vote-based aggregation extracted 22 advanced Parkinson's indicators from 1,201 patients' records, with 85.4% sensitivity and 94.6% NPV against neurologist annotation.
Diagnosis-date extraction remained the main limitation, and the authors recommend targeted clinical review for critical fields; precision was also only 61.4%.
Evidence
- Peer-reviewedIEEE Journal of Biomedical and Health Informatics2026-10-21
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Truvace Impact Record TRV-2026-2209, v1: “AI-Powered Structuring of Clinical Information in Parkinson's Disease: An Approach Based on Large Language Models.” Truvace, 2026-10-23. /record/TRV-2026-2209 (accessed at citation time). sha256 506989f3d2cd761f…
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