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Transforming Hypertension Diagnosis and Management in The Era of Artificial Intelligence: A 2023 National Heart, Lung, and Blood Institute (NHLBI) Workshop Report.
Shimbo, Daichi; Shah, Rashmee U; Abdalla, Marwah; Agarwal, Ritu; Ahmad, Faraz S; Anaya, Gabriel; Attia, Zachi I; Bull, Sheana; Chang, Alexander R; Commodore-Mensah, Yvonne; Ferdinand, Keith; Kawamoto, Kensaku; Khera, Rohan; Leopold, Jane; Luo, James; Makhni, Sonya; Mortazavi, Bobak J; Oh, Young S; Savage, Lucia C; Spatz, Erica S; Stergiou, George; Turakhia, Mintu P; Whelton, Paul K; Yancy, Clyde W; Iturriaga, Erin.
Afiliação
  • Shimbo D; Department of Medicine, Columbia University Irving Medical Center, New York, NY (D.S., M.A.).
  • Shah RU; Division of Cardiovascular Medicine, University of Utah School of Medicine, Salt Lake City. (R.U.S.).
  • Abdalla M; Department of Medicine, Columbia University Irving Medical Center, New York, NY (D.S., M.A.).
  • Agarwal R; Center for Digital Health and Artificial Intelligence, Johns Hopkins Carey Business School, Baltimore, MD (R.A.).
  • Ahmad FS; Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (F.S.A.).
  • Anaya G; Division of Cardiovascular Sciences, National Institutes of Health, National Heart, Lung and Blood Institute, Bethesda, MD (G.A., J.L., Y.S.O., E.I.).
  • Attia ZI; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN (Z.I.A.).
  • Bull S; Department of Community and Behavioral Health, Colorado School of Public Health, Aurora (S.B.).
  • Chang AR; Departments of Nephrology and Population Health Sciences, Geisinger, Danville, PA (A.R.C.).
  • Commodore-Mensah Y; Johns Hopkins School of Nursing and Bloomberg School of Public Health, Department of Epidemiology, Baltimore, MD (Y.C.-M.).
  • Ferdinand K; John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA. (K.F.).
  • Kawamoto K; Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City. (K.K.).
  • Khera R; Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT (R.K., E.S.S.).
  • Leopold J; Center for Outcomes Research and Evaluation, Yale New Haven Hospital, CT (R.K., E.S.S.).
  • Luo J; Section of Health Informatics, Department of Biostatistics (R.K.).
  • Makhni S; Yale University School of Public Health, New Haven, CT (R.K.).
  • Mortazavi BJ; Division of Cardiovascular Sciences, National Institutes of Health, National Heart, Lung and Blood Institute, Bethesda, MD (G.A., J.L., Y.S.O., E.I.).
  • Oh YS; Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (J.L.).
  • Savage LC; Department of Medicine, Columbia University Irving Medical Center, New York, NY (D.S., M.A.).
  • Spatz ES; Department of Medicine, University of Chicago Medicine and Biological Sciences Division, Chicago (S.M.).
  • Stergiou G; Department of Computer Science & Engineering, Texas A&M University, College Station (B.J.M.).
  • Turakhia MP; Yale School of Medicine, Yale University, New Haven, CT (B.J.M.).
  • Whelton PK; Division of Cardiovascular Sciences, National Institutes of Health, National Heart, Lung and Blood Institute, Bethesda, MD (G.A., J.L., Y.S.O., E.I.).
  • Yancy CW; Chief Privacy & Regulatory Officer, Omada Health, Inc, San Francisco, CA (L.C.S.).
  • Iturriaga E; Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT (R.K., E.S.S.).
Hypertension ; 2024 Jul 16.
Article em En | MEDLINE | ID: mdl-39011653
ABSTRACT
Hypertension is among the most important risk factors for cardiovascular disease, chronic kidney disease, and dementia. The artificial intelligence (AI) field is advancing quickly, and there has been little discussion on how AI could be leveraged for improving the diagnosis and management of hypertension. AI technologies, including machine learning tools, could alter the way we diagnose and manage hypertension, with potential impacts for improving individual and population health. The development of successful AI tools in public health and health care systems requires diverse types of expertise with collaborative relationships between clinicians, engineers, and data scientists. Unbiased data sources, management, and analyses remain a foundational challenge. From a diagnostic standpoint, machine learning tools may improve the measurement of blood pressure and be useful in the prediction of incident hypertension. To advance the management of hypertension, machine learning tools may be useful to find personalized treatments for patients using analytics to predict response to antihypertension medications and the risk for hypertension-related complications. However, there are real-world implementation challenges to using AI tools in hypertension. Herein, we summarize key findings from a diverse group of stakeholders who participated in a workshop held by the National Heart, Lung, and Blood Institute in March 2023. Workshop participants presented information on communication gaps between clinical medicine, data science, and engineering in health care; novel approaches to estimating BP, hypertension risk, and BP control; and real-world implementation challenges and issues.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Hypertension Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Hypertension Ano de publicação: 2024 Tipo de documento: Article