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

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.

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Meta-analysis and machine learning-augmented mixed effects cohort analysis of improved diets among 5,847 medical trainees, providers, and patients

Public Health Nutrition ◽

10.1017/s1368980021002809 ◽

2021 ◽

pp. 1-27

Author(s):

Dominique J. Monlezun ◽

Christopher Carr ◽

Tianhua Niu ◽

Francesco Nordio ◽

Nicole DeValle ◽

...

Keyword(s):

Machine Learning ◽

Cohort Study ◽

Nutrition Education ◽

Meta Analysis ◽

Cohort Analysis ◽

Cost Effective ◽

Number Needed To Treat ◽

Nutrition Counseling ◽

Medical Trainees ◽

Hands On

Abstract Objective: We sought to produce the first meta-analysis (of medical trainee competency improvement in nutrition counseling) informing the first cohort study of patient diet improvement through medical trainees and providers counseling patients on nutrition. Design: (Part A) A systematic review and meta-analysis informing (Part B) the intervention analyzed in the world’s largest prospective multi-center cohort study on hands-on cooking and nutrition education for medical trainees, providers, and patients. Settings: (A) Medical educational institutions. (B) Teaching kitchens. Participants: (A) Medical trainees. (B) Trainees, providers, and patients. Results: (A) Of the 212 citations identified (N=1,698 trainees), 11 studies met inclusion criteria. The overall effect size was 9.80 (95%CI 7.15-12.456.87-13.85; p<0.001), comparable to the machine learning (ML)-augmented results. The number needed to treat for the top performing high quality study was 12. (B) The hands-on cooking and nutrition education curriculum from the top performing study was applied for medical trainees and providers who subsequently taught patients in the same curriculum (N=5,847). The intervention compared to standard medical care and education alone significantly increased the odds of superior diets (high/medium versus low Mediterranean diet adherence) for residents/fellows most (OR 10.79, 95%CI 4.94-23.58; p<0.001) followed by students (OR 9.62, 95%CI 5.92-15.63; p<0.001), providers (OR 5.19, 95%CI 3.23-8.32, p<0.001), and patients (OR 2.48, 95%CI 1.38-4.45; p=0.002), results consistent with those from ML. Conclusions: This study suggests that medical trainees and providers can improve patients’ diets with nutrition counseling in a manner that is clinically and cost effective and may simultaneously advance societal equity.

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Novel Longitudinal and Propensity Score Matched Analysis of Hands-On Cooking and Nutrition Education versus Traditional Clinical Education among 627 Medical Students

Advances in Preventive Medicine ◽

10.1155/2015/656780 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 15

Author(s):

Dominique J. Monlezun ◽

Benjamin Leong ◽

Esther Joo ◽

Andrew G. Birkhead ◽

Leah Sarris ◽

...

Keyword(s):

Medical Students ◽

Propensity Score ◽

Medical School ◽

Nutrition Education ◽

Clinical Education ◽

Meta Analysis ◽

Traditional Education ◽

Inverse Variance ◽

Hands On ◽

Disease Epidemics

Background. Physicians are inadequately equipped to respond to the global obesity and nutrition-associated chronic disease epidemics. We investigated superiority of simulation-based medical education with deliberate practice (SBME-DP) hands-on cooking and nutrition elective in a medical school-based teaching kitchen versus traditional clinical education for medical students.Materials and Methods. A 59-question panel survey was distributed to an entire medical school twice annually from September 2012 to May 2014. Student diet and attitudes and competencies (DACs) counseling patients on nutrition were compared using conditional multivariate logistic regression, propensity score-weighted, and longitudinal panel analyses. Inverse-variance weighted meta-analysis (IVWM) was used for planned subgroup analysis by year and treatment estimates across the three methods.Results. Of the available 954 students, 65.72% (n=627) unique students were followed to produce 963 responses. 11.32% (n=109) of responses were from 84 subjects who participated in the elective. SBME-DP versus traditional education significantly improved fruit and vegetable diet (OR = 1.38, 95% CI: 1.07–1.79,p=0.013) and attitudes (OR = 1.81, 95% CI: 1.40–2.35,p<0.001) and competencies (OR = 1.72, 95% CI: 1.54–1.92,p<0.001).Conclusions. This study reports for the first time superiority longitudinally for SBME-DP style nutrition education for medical students which has since expanded to 13 schools.

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Culinary medicine and community partnership: hands-on culinary skills training to empower medical students to provide patient-centered nutrition education

Medical Education Online ◽

10.1080/10872981.2019.1630238 ◽

2019 ◽

Vol 24 (1) ◽

pp. 1630238 ◽

Cited By ~ 5

Author(s):

Brandon Pang ◽

Zoe Memel ◽

Carmel Diamant ◽

Emily Clarke ◽

Sherene Chou ◽

...

Keyword(s):

Medical Students ◽

Nutrition Education ◽

Skills Training ◽

Community Partnership ◽

Patient Centered ◽

Culinary Medicine ◽

Hands On

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Evaluation of an international medical E-learning course with natural language processing and machine learning

BMC Medical Education ◽

10.1186/s12909-021-02609-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Aditya Borakati

Keyword(s):

Machine Learning ◽

Cohort Study ◽

Natural Language Processing ◽

Medical Students ◽

Text Mining ◽

Natural Language ◽

Language Processing ◽

Small Scale ◽

E Learning ◽

The Impact

Abstract Background In the context of the ongoing pandemic, e-learning has become essential to maintain existing medical educational programmes. Evaluation of such courses has thus far been on a small scale at single institutions. Further, systematic appraisal of the large volume of qualitative feedback generated by massive online e-learning courses manually is time consuming. This study aimed to evaluate the impact of an e-learning course targeting medical students collaborating in an international cohort study, with semi-automated analysis of feedback using text mining and machine learning methods. Method This study was based on a multi-centre cohort study exploring gastrointestinal recovery following elective colorectal surgery. Collaborators were invited to complete a series of e-learning modules on key aspects of the study and complete a feedback questionnaire on the modules. Quantitative data were analysed using simple descriptive statistics. Qualitative data were analysed using text mining with most frequent words, sentiment analysis with the AFINN-111 and syuzhet lexicons and topic modelling using the Latent Dirichlet Allocation (LDA). Results One thousand six hundred and eleventh collaborators from 24 countries completed the e-learning course; 1396 (86.7%) were medical students; 1067 (66.2%) entered feedback. 1031 (96.6%) rated the quality of the course a 4/5 or higher (mean 4.56; SD 0.58). The mean sentiment score using the AFINN was + 1.54/5 (5: most positive; SD 1.19) and + 0.287/1 (1: most positive; SD 0.390) using syuzhet. LDA generated topics consolidated into the themes: (1) ease of use, (2) conciseness and (3) interactivity. Conclusions E-learning can have high user satisfaction for training investigators of clinical studies and medical students. Natural language processing may be beneficial in analysis of large scale educational courses.

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Regional variations in medical trainee diet and nutrition counseling competencies: Machine learning-augmented propensity score analysis of a prospective multi-site cohort study

Medical Science Educator ◽

10.1007/s40670-020-00973-6 ◽

2020 ◽

Vol 30 (2) ◽

pp. 911-915

Author(s):

Anish Patnaik ◽

Justin Tran ◽

John W. McWhorter ◽

Helen Burks ◽

Alexandra Ngo ◽

...

Keyword(s):

Machine Learning ◽

Cohort Study ◽

Propensity Score ◽

Propensity Score Analysis ◽

Nutrition Counseling ◽

Counseling Competencies ◽

Regional Variations ◽

Medical Trainee ◽

Score Analysis

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Evaluation of an International Medical E-Learning Course with Natural Language Processing and Machine Learning

10.21203/rs.3.rs-116209/v1 ◽

2020 ◽

Author(s):

Aditya Borakati

Keyword(s):

Machine Learning ◽

Cohort Study ◽

Natural Language Processing ◽

Medical Students ◽

Text Mining ◽

Natural Language ◽

Language Processing ◽

Small Scale ◽

E Learning ◽

The Impact

Abstract Background: In the context of the ongoing pandemic, e-learning has become essential to maintain existing medical educational programmes. Evaluation of such courses has thus far been on a small scale at single institutions. Further, systematic appraisal of the large volume of qualitative feedback generated by massive online e-learning courses manually is time consuming. This study aimed to evaluate the impact of an e-learning course targeting medical students collaborating in an international cohort study, with semi-automated analysis of feedback using text mining and machine learning methods.Method: This study was based on a multi-centre cohort study exploring gastrointestinal recovery following elective colorectal surgery. Collaborators were invited to complete a series of e-learning modules on key aspects of the study and complete a feedback questionnaire on the modules. Quantitative data were analysed using simple descriptive statistics. Qualitative data were analysed using text mining with most frequent words, sentiment analysis with the AFINN-111 and syuzhet lexicons and topic modelling using the Latent Dirichlet Allocation (LDA). Results: 1,611 collaborators from 24 countries completed the e-learning course; 1,396 (86.7%) were medical students; 1,067 (66.2%) entered feedback. 1,031 (96.6%) rated the quality of the course a 4/5 or higher (mean 4.56; SD 0.58). The mean sentiment score using the AFINN was +1.54/5 (5: most positive; SD 1.19) and +0.287/1 (1: most positive; SD 0.390) using syuzhet. LDA generated topics consolidated into the themes: (1) ease of use, (2) conciseness and (3) interactivity.Conclusions: E-learning can have high user satisfaction for training investigators of clinical studies and medical students. Natural language processing may be beneficial in analysis of large scale educational courses.

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