  Today, despite decades of developments in medicine and the growing interest
in precision healthcare, vast majority of diagnoses happen once patients begin
to show noticeable signs of illness. Early indication and detection of
diseases, however, can provide patients and carers with the chance of early
intervention, better disease management, and efficient allocation of healthcare
resources. The latest developments in machine learning (more specifically, deep
learning) provides a great opportunity to address this unmet need. In this
study, we introduce BEHRT: A deep neural sequence transduction model for EHR
(electronic health records), capable of multitask prediction and disease
trajectory mapping. When trained and evaluated on the data from nearly 1.6
million individuals, BEHRT shows a striking absolute improvement of 8.0-10.8%,
in terms of Average Precision Score, compared to the existing state-of-the-art
deep EHR models (in terms of average precision, when predicting for the onset
of 301 conditions). In addition to its superior prediction power, BEHRT
provides a personalised view of disease trajectories through its attention
mechanism; its flexible architecture enables it to incorporate multiple
heterogeneous concepts (e.g., diagnosis, medication, measurements, and more) to
improve the accuracy of its predictions; and its (pre-)training results in
disease and patient representations that can help us get a step closer to
interpretable predictions.
