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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.12317 |
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| _version_ | 1866912277163147264 |
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| author | Rao, Shishir Ahmed, Nouman Salimi-Khorshidi, Gholamreza Yau, Christopher Su, Huimin Conrad, Nathalie Asselbergs, Folkert W Woodward, Mark Jackson, Rod Cleland, John GF Rahimi, Kazem |
| author_facet | Rao, Shishir Ahmed, Nouman Salimi-Khorshidi, Gholamreza Yau, Christopher Su, Huimin Conrad, Nathalie Asselbergs, Folkert W Woodward, Mark Jackson, Rod Cleland, John GF Rahimi, Kazem |
| contents | We developed and validated TRisk, a Transformer-based AI model predicting 36-month mortality in heart failure patients by analysing temporal patient journeys from UK electronic health records (EHR). Our study included 403,534 heart failure patients (ages 40-90) from 1,418 English general practices, with 1,063 practices for model derivation and 355 for external validation. TRisk was compared against the MAGGIC-EHR model across various patient subgroups. With median follow-up of 9 months, TRisk achieved a concordance index of 0.845 (95% confidence interval: [0.841, 0.849]), significantly outperforming MAGGIC-EHR's 0.728 (0.723, 0.733) for predicting 36-month all-cause mortality. TRisk showed more consistent performance across sex, age, and baseline characteristics, suggesting less bias. We successfully adapted TRisk to US hospital data through transfer learning, achieving a C-index of 0.802 (0.789, 0.816) with 21,767 patients. Explainability analyses revealed TRisk captured established risk factors while identifying underappreciated predictors like cancers and hepatic failure that were important across both cohorts. Notably, cancers maintained strong prognostic value even a decade after diagnosis. TRisk demonstrated well-calibrated mortality prediction across both healthcare systems. Our findings highlight the value of tracking longitudinal health profiles and revealed risk factors not included in previous expert-driven models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12317 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Transformer-based survival model for prediction of all-cause mortality in heart failure patients: a multi-cohort study Rao, Shishir Ahmed, Nouman Salimi-Khorshidi, Gholamreza Yau, Christopher Su, Huimin Conrad, Nathalie Asselbergs, Folkert W Woodward, Mark Jackson, Rod Cleland, John GF Rahimi, Kazem Artificial Intelligence We developed and validated TRisk, a Transformer-based AI model predicting 36-month mortality in heart failure patients by analysing temporal patient journeys from UK electronic health records (EHR). Our study included 403,534 heart failure patients (ages 40-90) from 1,418 English general practices, with 1,063 practices for model derivation and 355 for external validation. TRisk was compared against the MAGGIC-EHR model across various patient subgroups. With median follow-up of 9 months, TRisk achieved a concordance index of 0.845 (95% confidence interval: [0.841, 0.849]), significantly outperforming MAGGIC-EHR's 0.728 (0.723, 0.733) for predicting 36-month all-cause mortality. TRisk showed more consistent performance across sex, age, and baseline characteristics, suggesting less bias. We successfully adapted TRisk to US hospital data through transfer learning, achieving a C-index of 0.802 (0.789, 0.816) with 21,767 patients. Explainability analyses revealed TRisk captured established risk factors while identifying underappreciated predictors like cancers and hepatic failure that were important across both cohorts. Notably, cancers maintained strong prognostic value even a decade after diagnosis. TRisk demonstrated well-calibrated mortality prediction across both healthcare systems. Our findings highlight the value of tracking longitudinal health profiles and revealed risk factors not included in previous expert-driven models. |
| title | A Transformer-based survival model for prediction of all-cause mortality in heart failure patients: a multi-cohort study |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2503.12317 |