Machine Learning-Augmented Propensity Score-Adjusted Multilevel Mixed Effects Panel Analysis of Hands-On Cooking and Nutrition Education versus Traditional Curriculum for Medical Students as Preventive Cardiology: Multisite Cohort Study of 3,248 Trainees over 5 Years
Authors: Monlezun Dominique J. , Dart Lyn , Vanbeber Anne , Smith-Barbaro Peggy , Costilla Vanessa , Samuel Charlotte , Terregino Carol A. , Abali Emine Ercikan , Dollinger Beth , Baumgartner Nicole , Kramer Nicholas , Seelochan Alex , Taher Sabira , Deutchman Mark , Evans Meredith , Ellis Robert B. , Oyola Sonia , Maker-Clark Geeta , Dreibelbis Tomi , Budnick Isadore , Tran David , DeValle Nicole , Shepard Rachel , Chow Erika , Petrin Christine , Razavi Alexander , McGowan Casey , Grant Austin , Bird Mackenzie , Carry Connor , McGowan Glynis , McCullough Colleen , Berman Casey M. , Dotson Kerri , Niu Tianhua , Sarris Leah , Harlan Timothy S. , Co-investigators on behalf of the CHOP 
Published: April 2018

Background. Cardiovascular disease (CVD) annually claims more lives and costs more dollars than any other disease globally amid widening health disparities, despite the known significant reductions in this burden by low cost dietary changes. The world’s first medical school-based teaching kitchen therefore launched CHOP-Medical Students as the largest known multisite cohort study of hands-on cooking and nutrition education versus traditional curriculum for medical students. Methods. This analysis provides a novel integration of artificial intelligence-based machine learning (ML) with causal inference statistics. 43 ML automated algorithms were tested, with the top performer compared to triply robust propensity score-adjusted multilevel mixed effects regression panel analysis of longitudinal data. Inverse-variance weighted fixed effects meta-analysis pooled the individual estimates for competencies. Results. 3,248 unique medical trainees met study criteria from 20 medical schools nationally from August 1, 2012, to June 26, 2017, generating 4,026 completed validated surveys. ML analysis produced similar results to the causal inference statistics based on root mean squared error and accuracy. Hands-on cooking and nutrition education compared to traditional medical school curriculum significantly improved student competencies (OR 2.14, 95% CI 2.00–2.28, p<0.001) and MedDiet adherence (OR 1.40, 95% CI 1.07–1.84, p=0.015), while reducing trainees’ soft drink consumption (OR 0.56, 95% CI 0.37–0.85, p=0.007). Overall improved competencies were demonstrated from the initial study site through the scale-up of the intervention to 10 sites nationally (p<0.001). Discussion. This study provides the first machine learning-augmented causal inference analysis of a multisite cohort showing hands-on cooking and nutrition education for medical trainees improves their competencies counseling patients on nutrition, while improving students’ own diets. This study suggests that the public health and medical sectors can unite population health management and precision medicine for a sustainable model of next-generation health systems providing effective, equitable, accessible care beginning with reversing the CVD epidemic.