The National Institutes of Health funding for clinical research applying machine learning techniques in 2017

Amarnath R Annapureddy; Suveen Angraal; Cesar Caraballo; Alyssa Grimshaw; Chenxi Huang; Bobak J Mortazavi; Harlan M Krumholz

doi:10.1038/s41746-020-0223-9

The National Institutes of Health funding for clinical research applying machine learning techniques in 2017

NPJ Digit Med. 2020 Jan 31:3:13. doi: 10.1038/s41746-020-0223-9. eCollection 2020.

Authors

Amarnath R Annapureddy^{1

2}, Suveen Angraal^{1

3}, Cesar Caraballo¹, Alyssa Grimshaw⁴, Chenxi Huang¹, Bobak J Mortazavi⁵, Harlan M Krumholz^{1

2

6}

Affiliations

¹ 1Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT USA.
² 2Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT USA.
³ 3Department of Internal Medicine, University of Missouri Kansas City School of Medicine, Kansas City, MO USA.
⁴ 4Harvey Cushing/John Hay Whitney Medical Library, Yale University, New Haven, CT USA.
⁵ 5Department of Computer Science & Engineering, Texas A&M University, College Station, TX USA.
⁶ 6Department of Health Policy and Management, Yale School of Public Health, New Haven, CT USA.

Abstract

Machine learning (ML) techniques have become ubiquitous and indispensable for solving intricate problems in most disciplines. To determine the extent of funding for clinical research projects applying ML techniques by the National Institutes of Health (NIH) in 2017, we searched the NIH Research Portfolio Online Reporting Tools Expenditures and Results (RePORTER) system using relevant keywords. We identified 535 projects, which together received a total of $264 million, accounting for 2% of the NIH extramural budget for clinical research.

Keywords: Machine learning; Outcomes research.

Grants and funding

UL1 TR001863/TR/NCATS NIH HHS/United States