A global analysis of national cardiovascular disease control plans using a multi-agent artificial intelligence model.

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Bibliographic Details
Title: A global analysis of national cardiovascular disease control plans using a multi-agent artificial intelligence model.
Authors: Pearson H; Health Systems Innovation Lab, Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America., Kumar CJ; Health Systems Innovation Lab, Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America., Reddy CL; Health Systems Innovation Lab, Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America., LeBlanc ER; Harvard College, Harvard University, Cambridge, Massachusetts, United States of America., Atun R; Health Systems Innovation Lab, Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America.; Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.; Department of Global Health and Social Medicine, Harvard Medical School, Harvard University, Boston, Massachusetts, United States of America.; Faculty of Medicine, Imperial College London, London, United Kingdom.
Corporate Authors: With the CVD Control Collaborative
Source: PLOS digital health [PLOS Digit Health] 2026 Jun 01; Vol. 5 (6), pp. e0001447. Date of Electronic Publication: 2026 Jun 01 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: PLOS Country of Publication: United States NLM ID: 9918335064206676 Publication Model: eCollection Cited Medium: Internet ISSN: 2767-3170 (Electronic) Linking ISSN: 27673170 NLM ISO Abbreviation: PLOS Digit Health Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2767-3170
DOI:10.1371/journal.pdig.0001447