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.
Highlights
Machine learning (ML) is the study of computer science and statistics that focuses on recognizing patterns and making inferences by analyzing large amounts of data, potentially with no explicit assumptions about these patterns
Our study provides a snapshot of federal funding for clinical research projects using ML techniques in the United States
This study highlights the small proportion of National Institutes of Health (NIH) funding that is allotted for clinical research projects applying ML, techniques that have immense potential to transform health care and add to the ongoing debate about NIH funding priorities
Summary
Machine learning (ML) is the study of computer science and statistics that focuses on recognizing patterns and making inferences by analyzing large amounts of data, potentially with no explicit assumptions about these patterns. There is little knowledge about which funding agencies of the NIH fund the most clinical research applying ML, and which grant types are funded, such as research grants (R series) and career development grants (K series). This knowledge could guide investigators and academic medical centers to compile their applications competitively for the appropriate NIH centers to increase the probability of being funded. Such understanding could reveal gaps in the types of ML studies being funded, which might inform decisions about future funding. We sought to describe and characterize the recipients of NIH funding for ML in 2017
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