Editorial Commentary: Big Data and Machine Learning in Medicine

Abstract

Recent research using machine learning and data mining to determine predictors of prolonged opioid use after arthroscopic surgery showed that Artificial Neural Networks showed superior discrimination and calibration. Other machine learning algorithms, such as Naïve Bayes, XG Boost, Gradient Boosting Model, Random Forest, and Elastic Net, were also reliable despite slightly lower Brier scores and mean areas under the curve. Machine learning and data mining have limitations, however, and outputs are reliant on large sample sizes and the accuracy of big data. Poor-quality data and the lack of confounding variables are further limitations. There is no doubt that predictive modeling, artificial intelligence, machine learning, and data mining will become a major component of the physician's practice, and doctors of medicine and related researchers should become familiar with these techniques. Physicians require an understanding of data science for the following reasons: monitoring of large databases could allow early diagnosis of pathologic conditions in individual patients; multiparameter data can be used to assist in the development of care pathways; data visualization could help with interpretation of medical images; understanding artificial intelligence workflow and machine learning will help us with understanding early warning signs of disease; and data science will facilitate personalized medicine with which clinicians can predict treatment outcomes.