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Makarov, N. ; Bordukova, M. ; Quengdaeng, P ; Garger, D. ; Rodriguez-Esteban, R.* ; Schmich, F.* ; Menden, M.P.

Large language models forecast patient health trajectories enabling digital twins.

NPJ Digit. Med. 8:588 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Generative artificial intelligence is revolutionizing digital twin development, enabling virtual patient representations that predict health trajectories, with large language models (LLMs) showcasing untapped clinical forecasting potential. We developed the Digital Twin-Generative Pretrained Transformer (DT-GPT), extending LLM-based forecasting solutions to clinical trajectory prediction. DT-GPT leverages electronic health records without requiring data imputation or normalization and overcomes real-world data challenges such as missingness, noise, and limited sample sizes. Benchmarking on non-small cell lung cancer, intensive care unit, and Alzheimer's disease datasets, DT-GPT outperformed state-of-the-art machine learning models, reducing the scaled mean absolute error by 3.4%, 1.3% and 1.8%, respectively. It maintained distributions and cross-correlations of clinical variables, and demonstrated explainability through a human-interpretable interface. Additionally, DT-GPT's ability to perform zero-shot forecasting highlights potential advantages of LLMs as clinical forecasting platforms, proposing a path towards digital twin applications in clinical trials, treatment selection, and adverse event mitigation.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
ISSN (print) / ISBN 2398-6352
e-ISSN 2398-6352
Zeitschrift NPJ digital medicine
Quellenangaben Band: 8, Heft: 1, Seiten: , Artikelnummer: 588 Supplement: ,
Verlag Nature Publishing Group
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)