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

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.

Multisite Medical Student–Led Community Culinary Medicine Classes Improve Patients’ Diets: Machine Learning–Augmented Propensity Score–Adjusted Fixed Effects Cohort Analysis of 1381 Subjects

2019 ◽  
pp. 155982761989360 ◽  
Author(s):  
Zachary Stauber ◽  
Alexander C. Razavi ◽  
Leah Sarris ◽  
Timothy S. Harlan ◽  
Dominique J. Monlezun
Keyword(s):  
Machine Learning ◽  
Medical Student ◽  
Fixed Effects ◽  
Dietary Habits ◽  
Vegetable Intake ◽  
Eating Habits ◽  
Cohort Analysis ◽  
Cost Effective ◽  
Hands On ◽  
Culinary Education

Background. Healthy diet represents one of the largest single modifiable risk factors proven to decrease rates of obesity and associated chronic disease, but practical approaches to improving dietary habits through nutritional intervention are limited. Objective. To evaluate the effectiveness of a medical student–led, 6-week culinary course on participants’ dietary knowledge and behaviors, particularly focusing on the tenets of the Mediterranean diet (MedDiet). Design. This study is a prospective multisite cohort study evaluating the effects of a 6-week, hands-on community culinary education course offered at 3 sites. Participants’ knowledge of cooking skills, eating habits, and adherence to the MedDiet were evaluated using a survey prior to beginning and 6 weeks after the completion of the course. Analysis was conducted using multivariable regression to assess subjects’ diets, associated behaviors, and nutrition beliefs according to the number of classes to which they were exposed (0 to >6). Statistical results were then compared with the machine learning results to check statistical validity after selection of the top-performing algorithm from 43 supervised algorithms using 10-fold cross-validation with performance assessed according to accuracy, root relative square error, and root mean square error. Results. Among the 1381 participants, cooking classes significantly improved patients’ overall 9-point MedDiet adherence (β = 0.62, 95% CI 0.23-1.00, P = .002). Participants were more likely to meet MedDiet point requirements for fruit intake (odds ratio [OR] 2.77, 95% CI 1.46-5.23, P = .002), vegetable intake (OR 4.61, 95% CI 1.85-11.53, P = .001), legume intake (OR 2.48, 95% CI 1.45-4.26, P = .001), and olive oil use (OR 2.87, 95% CI 1.44-5.74, P = .003), and were less likely to believe that cooking takes excessive time (OR 0.31, 95% CI 0.16-0.59, P < .001). Conclusion. Hands-on culinary education courses were associated with increased MedDiet adherence and improved knowledge of healthful eating. Such interventions thus represent a cost-effective option for addressing rates of obesity and obesity-related chronic illness.

scholarly journals 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

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Dominique J. Monlezun ◽  
Lyn Dart ◽  
Anne Vanbeber ◽  
Peggy Smith-Barbaro ◽  
Vanessa Costilla ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cohort Study ◽  
Medical Students ◽  
Propensity Score ◽  
Causal Inference ◽  
Nutrition Education ◽  
Mixed Effects ◽  
Panel Analysis ◽  
Traditional Curriculum ◽  
Hands On

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.

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

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
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.

A systematic review and meta-analysis comparing the efficacy of cyanoacrylate ablation over endovenous thermal ablation for treating incompetent saphenous veins

2021 ◽  
pp. 026835552110087
Author(s):  
Julong Guo ◽  
Fan Zhang ◽  
Jianming Guo ◽  
Lianrui Guo ◽  
Yongquan Gu ◽  
...  
Keyword(s):  
Cohort Study ◽  
Thermal Ablation ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Compression Stocking ◽  
Number Needed To Treat ◽  
Saphenous Veins ◽  
Significant Difference ◽  
Endovenous Thermal Ablation

Objectives The aim of this study was to systemically review and analyze the efficacy of cyanoacrylate ablation (CA) in comparison with endovenous thermal ablation (ETA) for the treatment of incompetent saphenous veins. Methods A systematic literature search was conducted using databases of Pubmed, Embase, and Cochrane Library from the times of their inception to April 2020. Studies were selected based on inclusion and exclusion criteria after assessing the risk of bias in comparative studies with Cochrane and rating quality of evidence with the GRADE methodology. The meta-analysis was carried out using the Review Manager 5.4 program to conduct homogeneity tests. Results One cohort study and three randomized controlled trials (RCT), including a total of 1457 participants were included in the meta-analysis. ETA included endovenous laser ablation (ELVA) and radiofrequency ablation (RFA) in the selected studies. Comparison between CA and a combination of EVLA and RFA or RFA alone were carried out in two of RCTs, while comparison between CA with EVLA was conducted in one RCT and the cohort study. There was no statistical difference in closure rates between CA and ETA after pooled analysis. Similar symptom alleviation observed between different groups. However, the CA group showed a lower ecchymosis rate than RFA and a significantly lower incidence of adverse events, such as ecchymosis, phlebitis and paresthesia, than EVLA. Compared with ETA, the patients received CA treatment exhibited lower pain scores in a shorter procedure duration without needing compression stocking, returned to normal life sooner, and had significantly better quality of care. There was no significant difference in the number needed to treat for additional therapy after three months of follow-up between groups. Conclusions This meta-analysis indicates that CA has better overall outcomes than ETA and offers superior clinical benefits in the treatment of incompetent saphenous veins.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

Knee Muscle Force Estimating Model Using Machine Learning Approach

2020 ◽  
Author(s):  
Anurag Sohane ◽  
Ravinder Agarwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Muscle Force ◽  
Vastus Lateralis ◽  
Input Parameter ◽  
Research Work ◽  
Cost Effective ◽  
Coefficient Of Determination ◽  
Muscle Forces ◽  
Knee Muscle

Abstract Various simulation type tools and conventional algorithms are being used to determine knee muscle forces of human during dynamic movement. These all may be good for clinical uses, but have some drawbacks, such as higher computational times, muscle redundancy and less cost-effective solution. Recently, there has been an interest to develop supervised learning-based prediction model for the computationally demanding process. The present research work is used to develop a cost-effective and efficient machine learning (ML) based models to predict knee muscle force for clinical interventions for the given input parameter like height, mass and angle. A dataset of 500 human musculoskeletal, have been trained and tested using four different ML models to predict knee muscle force. This dataset has obtained from anybody modeling software using AnyPyTools, where human musculoskeletal has been utilized to perform squatting movement during inverse dynamic analysis. The result based on the datasets predicts that the random forest ML model outperforms than the other selected models: neural network, generalized linear model, decision tree in terms of mean square error (MSE), coefficient of determination (R2), and Correlation (r). The MSE of predicted vs actual muscle forces obtained from the random forest model for Biceps Femoris, Rectus Femoris, Vastus Medialis, Vastus Lateralis are 19.92, 9.06, 5.97, 5.46, Correlation are 0.94, 0.92, 0.92, 0.94 and R2 are 0.88, 0.84, 0.84 and 0.89 for the test dataset, respectively.

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