JUNE 2018CIOAPPLICATIONS.COM 19DR. CHRIS STOUT, VP, CLINICAL RESEARCH AND DATA ANALYTICS, ATI HOLDINGS, LLCHOW PREDICTIVE ANALYTICS AND MACHINE LEARNING ARE CHANGING HEALTHCARErom my daily focus on the analytical realm of sports medicine and orthopedic rehabilitation, I am very interested in and have published on complex systems and nonlinear relationships in hospitals' functioning and healthcare. With almost daily improvement and refinement of using big data sets and their being more available (see Registries below), we can move away from the prior orthodoxy of IFTTT (IF This Then That) approach of causality to a more sophisticated (and realistic) one that considers risk pattern recognition over that of solely risk factors. Initial work in this area is in sports injury understanding and prevention, but conceivably is scalable to public health and personalized medicine.Predictive AnalyticsThe authors of a paper, Complex Systems Approach for Sports Injuries, note that "Injury prediction is one of the most challenging issues in sports and a key component for injury prevention. Sports injuries aetiology investigations have assumed a reductionist view in which a phenomenon has been simplified into units and analyzed as the sum of its basic parts and causality has been seen in a linear and unidirectional way. This reductionist approach relies on correlation and regression analyses and, despite the vast effort to predict sports injuries, it has been limited in its ability to successfully identify predictive factors. The majority of human health conditions are complex. In this sense, the multifactorial complex nature of sports injuries arises not from the linear interaction between isolated and predictive factors, but from the complex interaction among a web of determinants."Other researchers looked at using predictive analytical tools to forecast injury likelihood in rugby players by informing as to a player's training load "...in order to field the best possible team throughout the season. The analysis (were used to) predict the likelihood of a particular player being injured, which then enabled the coaching team to adapt and modify each player's personalized training program to maximize their training load and minimize their risk of injury."Sports Injury Predictor is a patent pending algorithm that determines the probability of an American football player being injured in a season. It applies machine learning and combined player injury data that includes "every injury that has taken place to skill position players in the NFL and college for the last 10 years. Includes type of injury, games missed, surgery required and more." It combines that with player age, height, weight, "position, how many times players will touch the ball in a game, number of plays a player is on the field" and then runs an "injury correlation matrix to determine the statistical probability of an injury occurring based on previous injury."Machine LearningIt was just in 2016 that one of the most prestigious medical journals, the Journal of the American Medical Association, made mention of machine learning. In the somewhat landmark paper, the authors speak to the internet of things and quantified-self in that, "Global adoption of mobile and wearable technology has added yet another dimension to machine learning, allowing the uploading of large amounts of personal data into learning algorithms. Now, within closed-loop feedback Dr. Chris StoutFcXo insights
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