Learning the natural history of human disease with generative transformers

Abstract Decision-making in healthcare relies on understanding patients’ past and current health states to predict and, ultimately, change their future course 1–3 . Artificial intelligence (AI) methods promise to aid this task by learning patterns of disease progression from large corpora of health records 4,5 . However, their potential has not been fully investigated at scale. Here we modify the GPT 6 (generative pretrained transformer) architecture to model the progression and competing nature of human diseases. We train this model, Delphi-2M, on data from 0.4 million UK Biobank participants and validate it using external data from 1.9 million Danish individuals with no change in parameters. Delphi-2M…