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Machine Learning in a Molecular Modeling Course for Chemistry, Biochemistry, and Biophysics Students.

ABSTRACT: Recent advances in computer hardware and software, particularly the availability of machine learning libraries, allow the introduction of data-based topics such as machine learning into the Biophysical curriculum for undergraduate and/or graduate levels. However, there are many practical challenges of teaching machine learning to advanced-level students in the biophysics majors, who often do not have a rich computational background. Aiming to overcome such challenges, we present an educational study, including the design of course topics, pedagogical tools, and assessments of student learning, to develop the new methodology to incorporate the basis of machine learning in an existing Biophysical elective course, and engage students in exercises to solve problems in an interdisciplinary field. In general, we observed that students had ample curiosity to learn and apply machine learning algorithms to predict molecular properties. Notably, feedback from the students suggests that care must be taken to ensure student preparations for understanding the data-driven concepts and fundamental coding aspects required for using machine learning algorithms. This work establishes a framework for future teaching approaches that unite machine learning and any existing course in the biophysical curriculum, while also pinpointing the critical challenges that educators and students will likely face.

SUBMITTER: Remington JM

PROVIDER: S-EPMC8323870 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background:Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions. Objective:To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile. Materials:An all-cause admissions dataset has been collected along six consecutive years in a pediatric hospital in Santiago, Chile. The variables collected are the same used for the determination of the child's treatment administrative cost. Methods:Retrospective predictive analysis of 30-day readmission was formulated as a binary classification problem. We report classification results achieved with various model building approaches after data curation and preprocessing for correction of class imbalance. We compute repeated cross-validation (RCV) with decreasing number of folders to assess performance and sensitivity to effect of imbalance in the test set and training set size. Results:Increase in recall due to SMOTE class imbalance correction is large and statistically significant. The Naive Bayes (NB) approach achieves the best AUC (0.65); however the shallow multilayer perceptron has the best PPV and f-score (5.6 and 10.2, resp.). The NB and support vector machines (SVM) give comparable results if we consider AUC, PPV, and f-score ranking for all RCV experiments. High recall of deep multilayer perceptron is due to high false positive ratio. There is no detectable effect of the number of folds in the RCV on the predictive performance of the algorithms. Conclusions:We recommend the use of Naive Bayes (NB) with Gaussian distribution model as the most robust modeling approach for pediatric readmission prediction, achieving the best results across all training dataset sizes. The results show that the approach could be applied to detect preventable readmissions.

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Machine Learning in a Molecular Modeling Course for Chemistry, Biochemistry, and Biophysics Students.

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