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Susan Kamal

Dados Biográficos

ID6778460
NOMESusan Kamal
PRENOMESSusan
SOBRENOMEKamal
ASSINATURAKAMAL S
AFILIAÇÕESPolyclinic Medical University
ORCID0000-0001-9114-5667
VERIFICADOSim
TOTAL DE OBRAS2
TOTAL DE CITAÇÕES0
TOTAL COMO AUTOR2
TOTAL COMO EDITOR0
PRIMEIRO ANO DE PUBLICAÇÃO2015
ANO MAIS RECENTE DE PUBLICAÇÃO2021
ÍNDICE H0
  • Random forest machine learning algorithm predicts virologic outcomes among HIV infected adults in Lausanne, Switzerland using electronically monitored combined antiretroviral treatment adherence

    Susan Kamal, John Urata et al.•ARTICLE•AIDS Care•2021

    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 medi…

  • Medication Adherence Programme at PMU Lausanne - A case study on People Centred and Integrated Health-Care Services (PCIHCS) Approach / Programa de Adhesión a Medicamentos al PMU Lausanne - Un estudio…

    Open Access•Susan Kamal, Olivier Bugnon et al.•ARTICLE•International Journal of…•2015

    Introduction: Despite many achievements in the past decade, health care systems in Europe are facing some challenges because of the epidemiological, demographic and economic transition in particular the burden of an aging population, which dictates a greater demand for care due to multiple co-morbidities and long term continuous care. This leads to concerns about the rising healthcare costs and cost-efficiency of the provided health services. On …

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  • Medication Adherence Programme at PMU Lausanne - A case study on People Centred and Integrated Health-Care Services (PCIHCS) Approach / Programa de Adhesión a Medicamentos al PMU Lausanne - Un estudio…

    Open Access•Susan Kamal, Olivier Bugnon et al.•ARTICLE•International Journal of…•2015

    Introduction: Despite many achievements in the past decade, health care systems in Europe are facing some challenges because of the epidemiological, demographic and economic transition in particular the burden of an aging population, which dictates a greater demand for care due to multiple co-morbidities and long term continuous care. This leads to concerns about the rising healthcare costs and cost-efficiency of the provided health services. On …

  • Random forest machine learning algorithm predicts virologic outcomes among HIV infected adults in Lausanne, Switzerland using electronically monitored combined antiretroviral treatment adherence

    Susan Kamal, John Urata et al.•ARTICLE•AIDS Care•2021

    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 medi…

Medicine (2 obras) · Algorithm (1 obras) · Ambulatory care (1 obras) · Antiretroviral therapy (1 obras) · Artificial Intelligence (1 obras) · Business (1 obras) · Chronic Disease Management Strategies (1 obras) · Cohort (1 obras) · Cohort study (1 obras) · Computer Science (1 obras)

Ethnos_APP • Projeto Open Source • Licença MIT • Frontend v2.0.0 • Privacidade e Cookies • Documentação da API: api.ethnos.app/docs • Código da API: GitHub • DOI: 10.5281/zenodo.17049435 • Código do Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae