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Worldwide, many millions of people die suddenly and unexpectedly each year,either with or without a prior history of cardiovascular disease. Such eventsare sparse (once in a lifetime), many victims will not have had priorinvestigations for cardiac disease and many different definitions of suddendeath exist. Accordingly, sudden death is hard to predict.  This analysis used NHS Electronic Health Records (EHRs) for people aged$\geq$50 years living in the Greater Glasgow and Clyde (GG\&C) region in 2010(n = 380,000) to try to overcome these challenges. We investigated whethermedical history, blood tests, prescription of medicines, and hospitalisationsmight, in combination, predict a heightened risk of sudden death.  We compared the performance of models trained to predict either sudden deathor all-cause mortality. We built six models for each outcome of interest: threetaken from state-of-the-art research (BEHRT, Deepr and Deep Patient), and threeof our own creation. We trained these using two different data representations:a language-based representation, and a sparse temporal matrix.  We used global interpretability to understand the most important features ofeach model, and compare how much agreement there was amongst models using RankBiased Overlap. It is challenging to account for correlated variables withoutincreasing the complexity of the interpretability technique. We overcame thisby clustering features into groups and comparing the most important groups foreach model. We found the agreement between models to be much higher whenaccounting for correlated variables.  Our analysis emphasises the challenge of predicting sudden death andemphasises the need for better understanding and interpretation of machinelearning models applied to healthcare applications.

Consensus of state of the art mortality prediction models: From all-cause mortality to sudden death prediction