Random forest machine learning algorithm predicts virologic outcomes among HIV infected adults in Lausanne, Switzerland using electronically monitored combined antiretroviral treatment adherence.
Machine Learning (ML) can improve the analysis of complex and interrelated factors that place adherent people at risk of viral rebound. Our aim was to build ML model to predict RNA viral rebound from medication adherence and clinical data. Patients were followed up at the Swiss interprofessional med...
| Publicado en: | AIDS Care Vol. 33; no. 4; pp. 530 - 537 |
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| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Apr2021
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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=149150194&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149150194 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09540121 AAE jtl: AIDS Care issn: 09540121 maglogo: N pubinfo: dt: Apr2021 vid: 33 iid: 4 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 149150194 145561294 149150194 149150194 10.1080/09540121.2020.1751045 149150194 ppf: 530 ppct: 7 formats: tig: atl: Random forest machine learning algorithm predicts virologic outcomes among HIV infected adults in Lausanne, Switzerland using electronically monitored combined antiretroviral treatment adherence. aug: au: Kamal, Susan Urata, John Cavassini, Matthias Liu, Honghu Kouyos, Roger Bugnon, Olivier Wang, Wei Schneider, Marie-Paule affil: Community pharmacy, School of pharmaceutical sciences, University of Geneva, University of Lausanne, Lausanne, Switzerland sug: subj: Machine Learning Algorithms HIV Infections Drug Therapy Anti-Retroviral Agents Medication Compliance Random Forest Viral Load Human Models, Theoretical RNA Treatment Outcomes Patient Compliance Switzerland Retrospective Design Male Female Adult CD4 Lymphocyte Count Drug Monitoring Methods Predictive Value of Tests Adult: 19-44 years Male Female ab: Machine Learning (ML) can improve the analysis of complex and interrelated factors that place adherent people at risk of viral rebound. Our aim was to build ML model to predict RNA viral rebound from medication adherence and clinical data. Patients were followed up at the Swiss interprofessional medication adherence program (IMAP). Sociodemographic and clinical variables were retrieved from the Swiss HIV Cohort Study (SHCS). Daily electronic medication adherence between 2008–2016 were analyzed retrospectively. Predictor variables included: RNA viral load (VL), CD4 count, duration of ART, and adherence. Random Forest, was used with 10 fold cross validation to predict the RNA class for each data observation. Classification accuracy metrics were calculated for each of the 10-fold cross validation holdout datasets. The values for each range from 0 to 1 (better accuracy). 383 HIV+ patients, 56% male, 52% white, median (Q1, Q3): age 43 (36, 50), duration of electronic monitoring of adherence 564 (200, 1333) days, CD4 count 406 (209, 533) cells/mm3, time since HIV diagnosis was 8.4 (4, 13.5) years, were included. Average model classification accuracy metrics (AUC and F1) for RNA VL were 0.6465 and 0.7772, respectively. In conclusion, combining adherence with other clinical predictors improve predictions of RNA. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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