Classification of the Severity of Adverse Drugs Reactions...30th Medical Informatics Europe Conference
This poster presents a non-exhaustive study of machine learning classification algorithms on pharmacovigilance data. In this study, we have taken into account the patient's clinical data such as medical history, medications taken and their indications for prescriptions, and the observed side effects...
| Publicado en: | Studies in Health Technology & Informatics Vol. 270; pp. 1227 - 1229 |
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| Autores principales: | , , , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2020
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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=144555450&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144555450 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2020 vid: 270 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 144555450 144555450 144555450 10.3233/SHTI200375 144555450 ppf: 1227 ppct: 2 formats: tig: atl: Classification of the Severity of Adverse Drugs Reactions...30th Medical Informatics Europe Conference aug: au: CHAUVET, Raphaël BOUSQUET, Cédric LILLOLELOUET, Agnès ZANA, Ilan KIMOUN, Ilan BEN JAULENT, MarieChristine affil: Sorbonne Université, INSERM, Université Paris 13, Laboratoire d’Informatique Médicale et d’Ingénierie des Connaissances en e-Santé, Paris, France. sug: subj: Adverse Drug Event Classification Machine Learning Pharmacovigilance Severity of Illness Decision Support Systems, Clinical Congresses and Conferences Human Drug Monitoring Algorithms Precision Polypharmacy ab: This poster presents a non-exhaustive study of machine learning classification algorithms on pharmacovigilance data. In this study, we have taken into account the patient's clinical data such as medical history, medications taken and their indications for prescriptions, and the observed side effects. From these elements we determine whether the patient case is considered serious or not. We show the performances of the different algorithms by their precision, recall and accuracy as well as their learning curves. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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