Machine learning in sports medicine: need for improvement

Abstract

![Graphic][1]</img> The over-riding goal of a physician is to optimise the outcome for each individual patient. However, our ability to manipulate the end result at the individual level is limited by our inability to accurately predict the expected outcome of a given clinical scenario. In the age of big data, machine learning can make our predictive capability both easier and more accurate using existing registries and databases which hold the potential to dramatically change decision-making and to optimise individual outcome. The purpose of this editorial was to explore the possible uses of machine learning in sports medicine using existing knee ligament registries as an example. Machine learning is a subset branch of artificial intelligence that uses data to make informed decisions/models without explicit programming (figure 1). Deep learning is a further subset of machine learning that uses neural networks to do the same task. Typically, once the data are acquired, significant time is spent preparing and formatting the data to be analysed, which includes removing or imputing variables which have too many missing values, standardising data for analysis and running standard statistical tests to assess relationships, such as collinearity (figure 1). Thereafter, the data are usually split into training, validation and testing data. The training data are most … [1]: /embed/inline-graphic-1.gif

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