Subject Matter Knowledge in the Age of Big Data and Machine Learning

The traditional paradigm of clinical research involves the analysis of well-curated data sets. In its ideal form, the theoretical underpinnings of associations between exposures and outcomes would be evaluated by collecting and analyzing data to evaluate a priori hypotheses. As clinical research catches up with other fields and finds itself immersed in the era of big data, the opportunity to apply more computational and data-driven techniques increases. While these techniques date back to neural networks proposed in the 1950s, it is only with recent advances in computing hardware that their full potential has been realized. Machine learning and, most recently, deep learning have become the standard bearers for modern computational methods. These approaches were first used in nonmedical fields where data were readily available, and now they are leveraged to conduct clinical research.