  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.
