Machine learning in clinical and epidemiological research: Isn't it time for biostatisticians to work on it?

In recent years, there has been a widespread cross-fertilization between Medical Statistics and Machine Learning (ML) techniques A broad range of ML methods are increasingly being used in many medical fields, such as oncology, internal medicine, cardiology, pediatrics, and genetics with a particular focus on the development of prediction tools. For example, in personalized medicine ML techniques have been used to derive the probability of treatment response for each patient (5). In oncology and cardiology, the ML approach has focused on prognosis and risk estimation (6,7). Moreover, ML approaches have been often applied to guide treatment decisions, to counsel patients, and to address the critical steps of clinical trials design (8). Despite its popularity, it is difficult to find a universally agreed-upon definition for ML. It is widely recognized that the major difference between ML and a traditional statistical approach lies in their purpose. ML methods are focused on making predictions as accurate as possible, whereas statistical models are aimed at inferring relationships between variables. However, many statistical models can make predictions too. On the other hand, ML techniques can provide different degrees of interpretability, from neural networks, which sacrifice interpretability to predictive power, to the highly interpretable lasso regression approach