Machine learning for improving high‐dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature-Reference-Cited by-同舟云学术

Machine learning for improving high‐dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature

Published:2022-07-05 Issue:9 Volume:31 Page:932-943
ISSN:1053-8569
Container-title:Pharmacoepidemiology and Drug Safety
language:en
Short-container-title:Pharmacoepidemiology and Drug

Author:

Wyss Richard¹,Yanover Chen²,El‐Hay Tal²³,Bennett Dimitri⁴^ORCID,Platt Robert W.⁵^ORCID,Zullo Andrew R.⁶^ORCID,Sari Grammati⁷,Wen Xuerong⁸^ORCID,Ye Yizhou⁹,Yuan Hongbo¹⁰,Gokhale Mugdha¹¹,Patorno Elisabetta¹,Lin Kueiyu Joshua¹¹²

Affiliation:

1. Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital Harvard Medical School Boston Massachusetts USA

2. KI Research Institute Kfar Malal Israel

3. IBM Research–Haifa Labs Haifa Israel

4. Global Evidence and Outcomes Takeda Pharmaceutical Company Ltd. Cambridge Massachusetts USA

5. Department of Epidemiology, Biostatistics, and Occupational Health McGIl University Montreal Canada

6. Department of Health Services, Policy, and Practice Brown University School of Public Health and Center of Innovation in Long‐Term Services and Supports, Providence Veterans Affairs Medical Center Providence Rhode Island USA

7. Real World Evidence Strategy Lead Visible Analytics Ltd Oxford UK

8. Health Outcomes, Pharmacy Practice, College of Pharmacy University of Rhode Island Kingston Rhode Island USA

9. Global Epidemiology AbbVie Inc. Illinois USA

10. Canadian Agency for Drugs and Technologies in Health Ottawa Canada

11. Pharmacoepidemiology Center for Observational and Real‐world Evidence Merck Pennsylvania USA

12. Department of Medicine Massachusetts General Hospital and Harvard Medical School Boston Massachusetts USA

Publisher

Wiley

Subject

Pharmacology (medical),Epidemiology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/pds.5500