Personalized decision making for coronary artery disease treatment using offline reinforcement learning
Abstract Choosing optimal revascularization strategies for patients with obstructive coronary artery disease (CAD) remains a clinical challenge. While randomized controlled trials offer population-level insights, gaps remain regarding personalized decision-making for individual patients. We applied...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01498-1 |
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| author | Peyman Ghasemi Matthew Greenberg Danielle A. Southern Bing Li James A. White Joon Lee |
| author_facet | Peyman Ghasemi Matthew Greenberg Danielle A. Southern Bing Li James A. White Joon Lee |
| author_sort | Peyman Ghasemi |
| collection | DOAJ |
| description | Abstract Choosing optimal revascularization strategies for patients with obstructive coronary artery disease (CAD) remains a clinical challenge. While randomized controlled trials offer population-level insights, gaps remain regarding personalized decision-making for individual patients. We applied off-policy reinforcement learning (RL) to a composite data model from 41,328 unique patients with angiography-confirmed obstructive CAD. In an offline setting, we estimated optimal treatment policies and evaluated these policies using weighted importance sampling. Our findings indicate that RL-guided therapy decisions outperformed physician-based decision making, with RL policies achieving up to 32% improvement in expected rewards based on composite major cardiovascular events outcomes. Additionally, we introduced methods to ensure that RL CAD treatment policies remain compatible with locally achievable clinical practice models, presenting an interpretable RL policy with a limited number of states. Overall, this novel RL-based clinical decision support tool, RL4CAD, demonstrates potential to optimize care in patients with obstructive CAD referred for invasive coronary angiography. |
| format | Article |
| id | doaj-art-0aea2c773ea644df931e3735c1efd114 |
| institution | DOAJ |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-0aea2c773ea644df931e3735c1efd1142025-08-20T02:48:27ZengNature Portfolionpj Digital Medicine2398-63522025-02-018111510.1038/s41746-025-01498-1Personalized decision making for coronary artery disease treatment using offline reinforcement learningPeyman Ghasemi0Matthew Greenberg1Danielle A. Southern2Bing Li3James A. White4Joon Lee5Data Intelligence for Health Lab, Cumming School of Medicine, University of CalgaryDepartment of Mathematics and Statistics, Faculty of Science, University of CalgaryCentre for Health Informatics, Cumming School of Medicine, University of CalgaryCentre for Health Informatics, Cumming School of Medicine, University of CalgaryDepartment of Cardiac Sciences, Cumming School of Medicine, University of CalgaryData Intelligence for Health Lab, Cumming School of Medicine, University of CalgaryAbstract Choosing optimal revascularization strategies for patients with obstructive coronary artery disease (CAD) remains a clinical challenge. While randomized controlled trials offer population-level insights, gaps remain regarding personalized decision-making for individual patients. We applied off-policy reinforcement learning (RL) to a composite data model from 41,328 unique patients with angiography-confirmed obstructive CAD. In an offline setting, we estimated optimal treatment policies and evaluated these policies using weighted importance sampling. Our findings indicate that RL-guided therapy decisions outperformed physician-based decision making, with RL policies achieving up to 32% improvement in expected rewards based on composite major cardiovascular events outcomes. Additionally, we introduced methods to ensure that RL CAD treatment policies remain compatible with locally achievable clinical practice models, presenting an interpretable RL policy with a limited number of states. Overall, this novel RL-based clinical decision support tool, RL4CAD, demonstrates potential to optimize care in patients with obstructive CAD referred for invasive coronary angiography.https://doi.org/10.1038/s41746-025-01498-1 |
| spellingShingle | Peyman Ghasemi Matthew Greenberg Danielle A. Southern Bing Li James A. White Joon Lee Personalized decision making for coronary artery disease treatment using offline reinforcement learning npj Digital Medicine |
| title | Personalized decision making for coronary artery disease treatment using offline reinforcement learning |
| title_full | Personalized decision making for coronary artery disease treatment using offline reinforcement learning |
| title_fullStr | Personalized decision making for coronary artery disease treatment using offline reinforcement learning |
| title_full_unstemmed | Personalized decision making for coronary artery disease treatment using offline reinforcement learning |
| title_short | Personalized decision making for coronary artery disease treatment using offline reinforcement learning |
| title_sort | personalized decision making for coronary artery disease treatment using offline reinforcement learning |
| url | https://doi.org/10.1038/s41746-025-01498-1 |
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