Introduction
To fulfill its mission, the NIH Office of Disease Prevention systematically monitors
NIH investments in applied prevention research. Specifically, the Office focuses on
research in humans involving primary and secondary prevention, and prevention-related
methods. Currently, the NIH uses the Research, Condition, and Disease Categorization
system to report agency funding in prevention research. However, this system defines
prevention research broadly to include primary and secondary prevention, studies on
prevention methods, and basic and preclinical studies for prevention. A new methodology
was needed to quantify NIH funding in applied prevention research.
Methods
A novel machine learning approach was developed and evaluated for its ability to characterize
NIH-funded applied prevention research during fiscal years 2012–2015. The sensitivity,
specificity, positive predictive value, accuracy, and F1 score of the machine learning
method; the Research, Condition, and Disease Categorization system; and a combined
approach were estimated. Analyses were completed during June–August 2017.
Results
Because the machine learning method was trained to recognize applied prevention research,
it more accurately identified applied prevention grants (F1 = 72.7%) than the Research,
Condition, and Disease Categorization system (F1 = 54.4%) and a combined approach
(F1 = 63.5%) with p<0.001.
Conclusions
This analysis demonstrated the use of machine learning as an efficient method to classify
NIH-funded research grants in disease prevention.
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Article Info
Publication History
Published online: October 25, 2018
Identification
Copyright
Published by Elsevier Inc. on behalf of American Journal of Preventive Medicine.
