Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Villani J

PROVIDER: S-EPMC6251715 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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

Villani Jennifer J Schully Sheri D SD Meyer Payam P Myles Ranell L RL Lee Jocelyn A JA Murray David M DM Vargas Ashley J AJ

American journal of preventive medicine 20181025 6

<h4>Introduction</h4>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 sec ...[more]

PMID: 30458951

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Project description:AimsTo define endotypes of carotid subclinical atherosclerosis.Methods and resultsWe integrated demographic, clinical, and molecular data (n = 124) with ultrasonographic carotid measurements from study participants in the IMPROVE cohort (n = 3340). We applied a neural network algorithm and hierarchical clustering to identify carotid atherosclerosis endotypes. A measure of carotid subclinical atherosclerosis, the c-IMTmean-max, was used to extract atherosclerosis-related features and SHapley Additive exPlanations (SHAP) to reveal endotypes. The association of endotypes with carotid ultrasonographic measurements at baseline, after 30 months, and with the 3-year atherosclerotic cardiovascular disease (ASCVD) risk was estimated by linear (β, SE) and Cox [hazard ratio (HR), 95% confidence interval (CI)] regression models. Crude estimates were adjusted by common cardiovascular risk factors, and baseline ultrasonographic measures. Improvement in ASCVD risk prediction was evaluated by C-statistic and by net reclassification improvement with reference to SCORE2, c-IMTmean-max, and presence of carotid plaques. An ensemble stacking model was used to predict endotypes in an independent validation cohort, the PIVUS (n = 1061). We identified four endotypes able to differentiate carotid atherosclerosis risk profiles from mild (endotype 1) to severe (endotype 4). SHAP identified endotype-shared variables (age, biological sex, and systolic blood pressure) and endotype-specific biomarkers. In the IMPROVE, as compared to endotype 1, endotype 4 associated with the thickest c-IMT at baseline (β, SE) 0.36 (0.014), the highest number of plaques 1.65 (0.075), the fastest c-IMT progression 0.06 (0.013), and the highest ASCVD risk (HR, 95% CI) (1.95, 1.18-3.23). Baseline and progression measures of carotid subclinical atherosclerosis and ASCVD risk were associated with the predicted endotypes in the PIVUS. Endotypes consistently improved measures of ASCVD risk discrimination and reclassification in both study populations.ConclusionsWe report four replicable subclinical carotid atherosclerosis-endotypes associated with progression of atherosclerosis and ASCVD risk in two independent populations. Our approach based on endotypes can be applied for precision medicine in ASCVD prevention.

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

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

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