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From Discharge Letters to Process Traces with LLMs: A Human-in-the-Loop Pipeline
Alessio Bottrighi1, Alessandro Canessa2, Delfina Ferrandi2
1Computer Science Institute, DiSIT, University of Eastern Piedmont, Alessandria, Italy.
None:
We present a modular human-in-the-loop pipeline that converts unstructured discharge letters into process-mining-ready event traces. The pipeline uses task-specific Large Language Model prompting to extract temporally ordered clinical events, followed by expert validation and iterative refinement. Validated traces are stored for PM analyses. The approach is currently evaluated on 466 Stroke Unit discharge letters from Alessandria Hospital.
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