Your browser doesn't support javascript.
loading
Machine learning in precision diabetes care and cardiovascular risk prediction.
Oikonomou, Evangelos K; Khera, Rohan.
Afiliação
  • Oikonomou EK; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Khera R; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA. rohan.khera@yale.edu.
Cardiovasc Diabetol ; 22(1): 259, 2023 09 25.
Article em En | MEDLINE | ID: mdl-37749579
Artificial intelligence and machine learning are driving a paradigm shift in medicine, promising data-driven, personalized solutions for managing diabetes and the excess cardiovascular risk it poses. In this comprehensive review of machine learning applications in the care of patients with diabetes at increased cardiovascular risk, we offer a broad overview of various data-driven methods and how they may be leveraged in developing predictive models for personalized care. We review existing as well as expected artificial intelligence solutions in the context of diagnosis, prognostication, phenotyping, and treatment of diabetes and its cardiovascular complications. In addition to discussing the key properties of such models that enable their successful application in complex risk prediction, we define challenges that arise from their misuse and the role of methodological standards in overcoming these limitations. We also identify key issues in equity and bias mitigation in healthcare and discuss how the current regulatory framework should ensure the efficacy and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes.
Assuntos
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doenças Cardiovasculares / Diabetes Mellitus Tipo de estudo: Diagnostic_studies / Etiology_studies / Guideline / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Cardiovasc Diabetol Assunto da revista: ANGIOLOGIA / CARDIOLOGIA / ENDOCRINOLOGIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doenças Cardiovasculares / Diabetes Mellitus Tipo de estudo: Diagnostic_studies / Etiology_studies / Guideline / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Cardiovasc Diabetol Assunto da revista: ANGIOLOGIA / CARDIOLOGIA / ENDOCRINOLOGIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos