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A Machine Learning Approach to Identify NIH-Funded Applied Prevention Research.

Jennifer Villani1, Sheri D Schully1, Payam Meyer2

  • 1Office of Disease Prevention, NIH, Rockville, Maryland.

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This summary is machine-generated.

A new machine learning method accurately identifies applied prevention research grants funded by the National Institutes of Health (NIH). This approach improves upon the existing Research, Condition, and Disease Categorization system for tracking disease prevention investments.

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Area of Science:

  • Public Health
  • Biomedical Research
  • Health Services Research

Background:

  • The National Institutes of Health (NIH) Office of Disease Prevention monitors investments in applied prevention research to support its mission.
  • Current methods, like the Research, Condition, and Disease Categorization (RCIDC) system, broadly define prevention research, necessitating a more precise approach.
  • Accurate quantification of NIH funding in applied prevention research is crucial for strategic planning and resource allocation.

Purpose of the Study:

  • To develop and evaluate a novel machine learning (ML) approach for characterizing NIH-funded applied prevention research.
  • To compare the performance of the ML method against the existing RCIDC system and a combined approach in identifying applied prevention grants.

Main Methods:

  • A machine learning model was developed to identify applied prevention research grants.
  • The model was trained and evaluated using NIH funding data from fiscal years 2012-2015.
  • Key performance metrics including sensitivity, specificity, positive predictive value, accuracy, and F1 score were calculated for the ML method, RCIDC, and a combined approach.

Main Results:

  • The machine learning method demonstrated superior performance in identifying applied prevention research grants, achieving an F1 score of 72.7%.
  • The Research, Condition, and Disease Categorization system had a lower F1 score of 54.4%.
  • A combined approach yielded an F1 score of 63.5%, which was also less accurate than the ML method (p<0.001).

Conclusions:

  • Machine learning offers an efficient and accurate method for classifying NIH-funded research grants in disease prevention.
  • This ML approach provides a more refined tool for monitoring and analyzing investments in applied prevention research.
  • The findings support the adoption of ML methodologies for improved tracking of public health research funding.