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An Explainable Transformer-Based Deep Learning Model for the Prediction of Incident Heart Failure

Lookup NU author(s): Dr Ali HassaineORCiD, Dr Dexter CanoyORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2013 IEEE.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 aimed to develop a deep-learning framework for accurate and yet explainable prediction of 6-month incident heart failure (HF). Using 100,071 patients from longitudinal linked electronic health records across the U.K., we applied a novel Transformer-based risk model using all community and hospital diagnoses and medications contextualized within the age and calendar year for each patient's clinical encounter. Feature importance was investigated with an ablation analysis to compare model performance when alternatively removing features and by comparing the variability of temporal representations. A post-hoc perturbation technique was conducted to propagate the changes in the input to the outcome for feature contribution analyses. Our model achieved 0.93 area under the receiver operator curve and 0.69 area under the precision-recall curve on internal 5-fold cross validation and outperformed existing deep learning models. Ablation analysis indicated medication is important for predicting HF risk, calendar year is more important than chronological age, which was further reinforced by temporal variability analysis. Contribution analyses identified risk factors that are closely related to HF. Many of them 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. In conclusion, the results highlight that our deep learning model, in addition high predictive performance, can inform data-driven risk factor identification.

Publication metadata

Author(s): Rao S, Li Y, Ramakrishnan R, Hassaine A, Canoy D, Cleland J, Lukasiewicz T, Salimi-Khorshidi G, Rahimi K

Publication type: Article

Publication status: Published

Journal: IEEE Journal of Biomedical and Health Informatics

Year: 2022

Volume: 26

Issue: 7

Pages: 3362-3372

Print publication date: 01/07/2022

Online publication date: 07/02/2022

Acceptance date: 25/01/2022

Date deposited: 24/11/2022

ISSN (print): 2168-2194

ISSN (electronic): 2168-2208

Publisher: Institute of Electrical and Electronics Engineers Inc.


DOI: 10.1109/JBHI.2022.3148820

PubMed id: 35130176


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