From Discharge Letters to Process Traces with LLMs: A Human-in-the-Loop Pipeline...36th Medical Informatics Europe (MIE) Conference, May 25-28, 2026, Genoa, Italy.
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. V...
| Publicado en: | Studies in Health Technology & Informatics Vol. 336; pp. 1037 - 1039 |
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| Autores principales: | , , , , , , , , |
| Formato: | pictorial proceedings tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194018976&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194018976 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2026 vid: 336 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 194018976 194018976 194018976 10.3233/SHTI260339 194018976 ppf: 1037 ppct: 2 formats: tig: atl: From Discharge Letters to Process Traces with LLMs: A Human-in-the-Loop Pipeline...36th Medical Informatics Europe (MIE) Conference, May 25-28, 2026, Genoa, Italy. aug: au: BOTTRIGHI, Alessio CANESSA, Alessandro FERRANDI, Delfina LEONARDI, Giorgio MACONI, Antonio MASSARINO, Costanza MONTANI, Stefania ROVETA, Annalisa STRIANI, Manuel affil: Computer Science Institute, DiSIT, University of Eastern Piedmont, Alessandria, Italy. sug: subj: Patient Discharge Summaries Documentation Data Mining Natural Language Processing Congresses and Conferences Italy Italy Daily Logs Workflow User-Computer Interface Data Quality ab: 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. pubtype: Academic Journal doctype: pictorial proceedings tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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