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.
Dominique J MonlezunLyn DartAnne VanbeberPeggy Smith-BarbaroVanessa CostillaCharlotte SamuelCarol A TerreginoEmine Ercikan AbaliBeth DollingerNicole BaumgartnerNicholas KramerAlex SeelochanSabira TaherMark DeutchmanMeredith EvansRobert B EllisSonia OyolaGeeta Maker-ClarkTomi DreibelbisIsadore BudnickDavid TranNicole DeValleRachel ShepardErika ChowChristine PetrinAlexander RazaviCasey McGowanAustin GrantMackenzie BirdConnor CarryGlynis McGowanColleen McCulloughCasey M BermanKerri DotsonTianhua NiuLeah SarrisTimothy S HarlanOn Behalf Of The Chop Co-InvestigatorsPublished in: BioMed research international (2018)
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.
Keyphrases
- healthcare
- machine learning
- public health
- quality improvement
- medical students
- physical activity
- newly diagnosed
- palliative care
- primary care
- weight loss
- artificial intelligence
- big data
- general practice
- deep learning
- patient reported outcomes
- human immunodeficiency virus
- smoking cessation
- prognostic factors
- acute kidney injury
- global health
- pain management