Clinical risk prediction with random forests for survival longitudinal and multivariate (RF-SLAM) data analysis

摘要

Clinical risk assessment has been a long-standing challenge in medicine, particularly at the individual level [ ]. Questions such as “what is the probability that this patient has a particular disease?” or “what is the probability that this patient will benefit from a particular treatment?” are difficult to answer objectively but are essential in order to realize the promise of precision medicine. Accurate clinical risk prediction can help guide decision making about health status, disease trajectory, and optimal treatment plans.