[2604.02337] Generating Counterfactual Patient Timelines from Real-World Data

Computer Science > Machine Learning arXiv:2604.02337 (cs) COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. [Submitted on 24 Jan 2026] Title:Generating Counterfactual Patient Timelines from Real-World Data Authors:Yu Akagi, Tomohisa Seki, Toru Takiguchi, Hiromasa Ito, Yoshimasa Kawazoe, Kazuhiko Ohe View a PDF of the paper titled Generating Counterfactual Patient Timelines from Real-World Data, by Yu Akagi and 5 other authors View PDF Abstract:Counterfactual simulation - exploring hypothetical consequences under alternative clinical scenarios - holds promise for transformative applications such as personalized medicine and in silico trials. However, it remains challenging due to methodological limitations. Here, we show that an autoregressive generative model trained on real-world data from over 300,000 patients and 400 million patient timeline entries can generate clinically plausible counterfactual trajectories. As a validation task, we applied the model to patients hospitalized with COVID-19 in 2023, modifying age, serum C-reactive protein (CRP), and serum creatinine to simulate 7-day outcomes. Increased in-hospital mortality was observed in counterfactual simulations with older age, elevated CRP, and elevated serum crea...