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 GPT⁶ (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 predicts the rates of more than 1,000 diseases, conditional on each

SUBMITTER: Shmatko A 

PROVIDER: S-EPMC12589094 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature