From Guidelines to Code: Formalizing STOPP/START Criteria Using LLMs and RAG for Clinical Decision Support...24th Special Topic Conference (STC 2025) of the European Federation for Medical Informatics (EFMI), October 20-22, 2025, Osnabruck, Germany
STOPP/START v3 is a set of criteria for optimizing therapy for elderly patients with polypharmacy. Implementing these criteria in prescribing software requires to formalize them, which is a difficult task. This project aimed to automate the formalization of these criteria using large language models...
| Publicado en: | Studies in Health Technology & Informatics Vol. 332; pp. 42 - 47 |
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| Autores principales: | , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2025
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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=188615440&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188615440 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2025 vid: 332 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 188615440 188615440 188615440 10.3233/SHTI251492 188615440 ppf: 42 ppct: 5 formats: tig: atl: From Guidelines to Code: Formalizing STOPP/START Criteria Using LLMs and RAG for Clinical Decision Support...24th Special Topic Conference (STC 2025) of the European Federation for Medical Informatics (EFMI), October 20-22, 2025, Osnabruck, Germany aug: au: ADROUJI, Samya MOUAZER, Abdelmalek LAMY, Jean-Baptise affil: Sorbonne Université, INSERM, Université Sorbonne Paris Nord, LIMICS, Paris France sug: subj: Practice Guidelines Natural Language Processing Utilization Decision Support Systems, Clinical Artificial Intelligence, Generative Programming Languages Human Algorithms Polypharmacy Coding Automation Information Retrieval Congresses and Conferences Germany Germany ab: STOPP/START v3 is a set of criteria for optimizing therapy for elderly patients with polypharmacy. Implementing these criteria in prescribing software requires to formalize them, which is a difficult task. This project aimed to automate the formalization of these criteria using large language models (LLMs), specifically leveraging Retrieval-Augmented Generation (RAG) for enhanced accuracy. We employed DeepSeek and GPT-4o-mini for entity extraction, code mapping to ICD-10, LOINC, and ATC, and the generation of executable Python code. A preliminary evaluation conducted on a subset of rules yielded a notably high F1-score (0.90, 0.92, 1 for drug, disease and observation entity mapping respectively and perfect results for medical entity extraction and code logic consistency). These results confirm the model's effectiveness in accurately transforming complex clinical rules into executable code. In conclusion, we successfully automated the creation of executable code from medical guidelines, proving that LLMs, supported by RAG, can be effective for automating clinical decision support tasks and formalizing medical rules. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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