A Perspective: Use of Machine Learning Models to Predict the Risk of Multimorbidity

Abstract

Machine Learning (ML) is a common Artificial Intelligence (AI) method. The use of ML offers the opportunity to develop better data mining techniques in order to analyse complex clinical interactions with a large number of variables. ML models should provide “real-time” clinical support reducing clinical risk to patients with model-agnostic interpretation to deduce a more specific clinical decision. Whilst ML algorithms have been used as the relatively “new kid on the block” in healthcare practice, they have shown promising results in predicting disease outcomes or risks in a variety of diseases such as depressive disorder, Type 2 diabetes mellitus, postoperative complications and cardiovascular diseases. However, patients suffering from a chronic condition are likely to have more than one condition requiring simultaneous attention and care. Therefore, a risk assessment model developed using ML methods, in theory, would be suitable to evaluate multimorbid populations. While there are many AI/ML algorithms and methods to build such a risk assessment tool, an optimal ‘fit-for-purpose’ model is chosen by comparing and contrasting across many possible alternatives. Further, given the high-stake decisions associated with health, it is also important that the model is interpretable and explainable by the clinicians who are purported to use such a model as their decision support system. In this paper, we provide a perspective on the current landscape of multimorbidity treatment, potential benefit of employing AI/ML to enhance holistic care of multimorbid patients, and associated challenges, concerns that need to be addressed as we make progress in this direction.