Technical Report: Machine-Learning Pipeline for Medical Research and Quality-Improvement Initiatives

Abstract

Machine-learning techniques have been increasing in popularity within medicine during the past decade. However, these computational techniques are not presented in statistical lectures throughout medical school and are perceived to have a high barrier to entry. The objective is to develop a concise pipeline with publicly available data to decrease the learning time towards using machine learning for medical research and quality-improvement initiatives. This report utilized a publicly available machine-learning data package in R (MLDataR) and computational packages (XGBoost) to highlight techniques for machine-learning model development and visualization with SHaply Additive exPlanations (SHAP). A simple six-step process along with example code was constructed to build and visualize machine-learning models. A concrete set of three steps was developed to help with interpretation. Further teaching of these methods could benefit researchers by providing alternative methods for data analysis in medical studies. These could help researchers without computational experience to get a feel for machine learning to better understand the literature and technique.

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