  Background: Electronic Health Records hold detailed longitudinal information
about each patient's health status and general clinical history, a large
portion of which is stored within the unstructured text. Existing approaches
focus mostly on structured data and a subset of single-domain outcomes. We
explore how temporal modelling of patients from free text and structured data,
using deep generative transformers can be used to forecast a wide range of
future disorders, substances, procedures or findings. Methods: We present
Foresight, a novel transformer-based pipeline that uses named entity
recognition and linking tools to convert document text into structured, coded
concepts, followed by providing probabilistic forecasts for future medical
events such as disorders, substances, procedures and findings. We processed the
entire free-text portion from three different hospital datasets totalling
811336 patients covering both physical and mental health. Findings: On tests in
two UK hospitals (King's College Hospital, South London and Maudsley) and the
US MIMIC-III dataset precision@10 0.68, 0.76 and 0.88 was achieved for
forecasting the next disorder in a patient timeline, while precision@10 of
0.80, 0.81 and 0.91 was achieved for forecasting the next biomedical concept.
Foresight was also validated on 34 synthetic patient timelines by five
clinicians and achieved relevancy of 97% for the top forecasted candidate
disorder. As a generative model, it can forecast follow-on biomedical concepts
for as many steps as required. Interpretation: Foresight is a general-purpose
model for biomedical concept modelling that can be used for real-world risk
forecasting, virtual trials and clinical research to study the progression of
disorders, simulate interventions and counterfactuals, and educational
purposes.
