Automatic Extraction of Medication Data from Semi-Structured Prescriptions...Medical Informatics Europe (MIE) 34th Conference, August 25–29, 2024, Athens, Greece.
In many healthcare facilities, the prescription of drugs is done only in a semi-structured manner, using free-text fields where various information is often mixed. Therefore, automatic processing, especially for secondary use such as research purposes, is often challenging. This paper compares vario...
| Publicado en: | Studies in Health Technology & Informatics Vol. 316; pp. 1694 - 1699 |
|---|---|
| Autores principales: | , , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2024
|
| 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=179286573&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179286573 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2024 vid: 316 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 179286573 179286573 179286573 10.3233/SHTI240749 179286573 ppf: 1694 ppct: 5 formats: tig: atl: Automatic Extraction of Medication Data from Semi-Structured Prescriptions...Medical Informatics Europe (MIE) 34th Conference, August 25–29, 2024, Athens, Greece. aug: au: OEHM, Johannes Benedict WENNING, Oliver STORCK, Michael Xiaoyi JIANG VARGHESE, Julian affil: Institute of Medical Informatics, University of Münster, Münster, Germany. sug: subj: Automation Drugs, Prescription Natural Language Processing Germany Human Congresses and Conferences Greece Greece Descriptive Statistics Machine Learning Algorithms Random Sample Germany ab: In many healthcare facilities, the prescription of drugs is done only in a semi-structured manner, using free-text fields where various information is often mixed. Therefore, automatic processing, especially for secondary use such as research purposes, is often challenging. This paper compares various approaches that identify and classify the various parts of these free-text fields in German language, namely simple Levenshtein-based, rule-based and CRF (conditional random field)-based approaches. Our results show that a F1-score >90% can be achieved with both the rule-based and the CRF-based approach, with the CRF-based approach even reaching nearly 95% pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|