High-Fidelity Longitudinal Patient Simulation Using Real-World Data
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866909999855304704 |
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| author | Akagi, Yu Seki, Tomohisa Ito, Hiromasa Takiguchi, Toru Ohe, Kazuhiko Kawazoe, Yoshimasa |
| author_facet | Akagi, Yu Seki, Tomohisa Ito, Hiromasa Takiguchi, Toru Ohe, Kazuhiko Kawazoe, Yoshimasa |
| contents | Simulation is a powerful tool for exploring uncertainty. Its potential in clinical medicine is transformative and includes personalized treatment planning and virtual clinical trials. However, simulating patient trajectories is challenging because of complex biological and sociocultural influences. Here, we show that real-world clinical records can be leveraged to empirically model patient timelines. We developed a generative simulator model that takes a patient's history as input and synthesizes fine-grained, realistic future trajectories. The model was pretrained on more than 200 million clinical records. It produced high-fidelity future timelines, closely matching event occurrence rates, laboratory test results, and temporal dynamics in real patient future data. It also accurately estimated future event probabilities, with observed-to-expected ratios consistently near 1.0 across diverse outcomes and time horizons. Our results reveal the untapped value of real-world data in electronic health records and introduce a scalable framework for in silico modeling of clinical care. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17310 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | High-Fidelity Longitudinal Patient Simulation Using Real-World Data Akagi, Yu Seki, Tomohisa Ito, Hiromasa Takiguchi, Toru Ohe, Kazuhiko Kawazoe, Yoshimasa Artificial Intelligence Machine Learning Simulation is a powerful tool for exploring uncertainty. Its potential in clinical medicine is transformative and includes personalized treatment planning and virtual clinical trials. However, simulating patient trajectories is challenging because of complex biological and sociocultural influences. Here, we show that real-world clinical records can be leveraged to empirically model patient timelines. We developed a generative simulator model that takes a patient's history as input and synthesizes fine-grained, realistic future trajectories. The model was pretrained on more than 200 million clinical records. It produced high-fidelity future timelines, closely matching event occurrence rates, laboratory test results, and temporal dynamics in real patient future data. It also accurately estimated future event probabilities, with observed-to-expected ratios consistently near 1.0 across diverse outcomes and time horizons. Our results reveal the untapped value of real-world data in electronic health records and introduce a scalable framework for in silico modeling of clinical care. |
| title | High-Fidelity Longitudinal Patient Simulation Using Real-World Data |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2601.17310 |