An explainable Transformer-based deep learning model for the prediction of incident heart failure

• URL: http://arxiv.org/abs/2101.11359v1
• Date: Wed, 27 Jan 2021 12:45:15 GMT
• Title: An explainable Transformer-based deep learning model for the prediction of incident heart failure
• Authors: Shishir Rao, Yikuan Li, Rema Ramakrishnan, Abdelaali Hassaine, Dexter Canoy, John Cleland, Thomas Lukasiewicz, Gholamreza Salimi-Khorshidi, Kazem Rahimi
• Abstract summary: We developed a novel Transformer deep-learning model for prediction of incident heart failure involving 100,071 patients. The model achieved 0.93 and 0.93 area under the receiver operator curve and 0.69 and 0.70 area under the precision-recall curve. The importance of contextualised medical information was revealed in sensitivity analyses.
• Score: 22.513476932615845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predicting the incidence of complex chronic conditions such as heart failure is challenging. Deep learning models applied to rich electronic health records may improve prediction but remain unexplainable hampering their wider use in medical practice. We developed a novel Transformer deep-learning model for more accurate and yet explainable prediction of incident heart failure involving 100,071 patients from longitudinal linked electronic health records across the UK. On internal 5-fold cross validation and held-out external validation, our model achieved 0.93 and 0.93 area under the receiver operator curve and 0.69 and 0.70 area under the precision-recall curve, respectively and outperformed existing deep learning models. Predictor groups included all community and hospital diagnoses and medications contextualised within the age and calendar year for each patient's clinical encounter. The importance of contextualised medical information was revealed in a number of sensitivity analyses, and our perturbation method provided a way of identifying factors contributing to risk. Many of the identified risk factors were consistent with existing knowledge from clinical and epidemiological research but several new associations were revealed which had not been considered in expert-driven risk prediction models.

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