Drug-related fall risk in hospitals: a machine learning approach.
Objective: To compare the performance of machine-learning models with the Medication Fall Risk Score (MFRS) in predicting fall risk related to prescription medications. Methods: This is a retrospective case-control study of adult and older adult patients in a tertiary hospital in Porto Alegre, RS, B...
| Publicado en: | Acta Paulista de Enfermagem Vol. 36; no. 1; pp. 1 - 8 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Universidade Federal de Sao Paulo, Escola Paulista de Enfermagem
2023
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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=180808737&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180808737 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01032100 YW2 jtl: Acta Paulista de Enfermagem issn: 01032100 maglogo: N pubinfo: dt: 2023 vid: 36 iid: 1 pid: 84124 pub: Universidade Federal de Sao Paulo, Escola Paulista de Enfermagem artinfo: ui: 180808737 10.37689/acta-ape/2023AO007711 180808737 ppf: 1 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Drug-related fall risk in hospitals: a machine learning approach. aug: au: Silva, Amanda Pestana da Santos, Henrique Dias Pereira dos Rotta, Ana Laura Olsefer Baiocco, Graziella Gasparotto Vieira, Renata Urbanetto, Janete de Souza affil: Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, RS, Brazil. sug: ab: Objective: To compare the performance of machine-learning models with the Medication Fall Risk Score (MFRS) in predicting fall risk related to prescription medications. Methods: This is a retrospective case-control study of adult and older adult patients in a tertiary hospital in Porto Alegre, RS, Brazil. Prescription drugs and drug classes were investigated. Data were exported to the RStudio software for statistical analysis. The variables were analyzed using Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting algorithms. Algorithm validation was performed using 10-fold cross validation. The Youden index was the metric selected to evaluate the models. The project was approved by the Research Ethics Committee. Results: The machine-learning model showing the best performance was the one developed by the Naive Bayes algorithm. The model built from a data set of a specific hospital showed better results for the studied population than did MFRS, a generalizable tool. Conclusion: Risk-prediction tools that depend on proper application and registration by professionals require time and attention that could be allocated to patient care. Prediction models built through machine-learning algorithms can help identify risks to improve patient care. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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