Predicting the risk of ischemic stroke in patients with atrial fibrillation using heterogeneous drug–protein–disease network-based deep learning
Current risk assessment models for predicting ischemic stroke (IS) in patients with atrial fibrillation (AF) often fail to account for the effects of medications and the complex interactions between drugs, proteins, and diseases. We developed an interpretable deep learning model, the AF-Biological-I...
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-06-01
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| Series: | APL Bioengineering |
| Online Access: | http://dx.doi.org/10.1063/5.0242570 |
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| author | Zhiheng Lyu Jiannan Yang Zhongzhi Xu Weilan Wang Weibin Cheng Kwok-Leung Tsui Qingpeng Zhang |
| author_facet | Zhiheng Lyu Jiannan Yang Zhongzhi Xu Weilan Wang Weibin Cheng Kwok-Leung Tsui Qingpeng Zhang |
| author_sort | Zhiheng Lyu |
| collection | DOAJ |
| description | Current risk assessment models for predicting ischemic stroke (IS) in patients with atrial fibrillation (AF) often fail to account for the effects of medications and the complex interactions between drugs, proteins, and diseases. We developed an interpretable deep learning model, the AF-Biological-IS-Path (ABioSPath), to predict one-year IS risk in AF patients by integrating drug–protein–disease pathways with real-world clinical data. Using a heterogeneous multilayer network, ABioSPath identifies mechanisms of drug actions and the propagation of comorbid diseases. By combining mechanistic pathways with patient-specific characteristics, the model provides individualized IS risk assessments and identifies potential molecular pathways involved. We utilized the electronic health record data from 7859 AF patients, collected between January 2008 and December 2009 across 43 hospitals in Hong Kong. ABioSPath outperformed baseline models in all evaluation metrics, achieving an AUROC of 0.7815 (95% CI: 0.7346–0.8283), a positive predictive value of 0.430, a negative predictive value of 0.870, a sensitivity of 0.500, a specificity of 0.885, an average precision of 0.409, and a Brier score of 0.195. Cohort-level analysis identified key proteins, such as CRP, REN, and PTGS2, within the most common pathways. Individual-level analysis further highlighted the importance of PIK3/Akt and cytokine and chemokine signaling pathways and identified IS risks associated with less-studied drugs like prochlorperazine maleate. ABioSPath offers a robust, data-driven approach for IS risk prediction, requiring only routinely collected clinical data without the need for costly biomarkers. Beyond IS, the model has potential applications in screening risks for other diseases, enhancing patient care, and providing insights for drug development. |
| format | Article |
| id | doaj-art-7c8cf53ba2c9441ca5f77872f7718810 |
| institution | OA Journals |
| issn | 2473-2877 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | APL Bioengineering |
| spelling | doaj-art-7c8cf53ba2c9441ca5f77872f77188102025-08-20T02:38:29ZengAIP Publishing LLCAPL Bioengineering2473-28772025-06-0192026104026104-1210.1063/5.0242570Predicting the risk of ischemic stroke in patients with atrial fibrillation using heterogeneous drug–protein–disease network-based deep learningZhiheng Lyu0Jiannan Yang1Zhongzhi Xu2Weilan Wang3Weibin Cheng4Kwok-Leung Tsui5Qingpeng Zhang6School of Public Health, Sun Yat-sen University, Guangzhou, ChinaMusketeers Foundation Institute of Data Science, The University of Hong Kong, Hong Kong SAR, ChinaSchool of Public Health, Sun Yat-sen University, Guangzhou, ChinaCentre for Healthy Longevity, Yong Loo Lin School of Medicine, National University of Singapore, SingaporeDepartment of Data Science, City University of Hong KongChinaDepartment of Manufacturing, Systems, and Industrial Engineering, University of Texas, Arlington, Texas 76019, USA;Musketeers Foundation Institute of Data Science, The University of Hong Kong, Hong Kong SAR, ChinaCurrent risk assessment models for predicting ischemic stroke (IS) in patients with atrial fibrillation (AF) often fail to account for the effects of medications and the complex interactions between drugs, proteins, and diseases. We developed an interpretable deep learning model, the AF-Biological-IS-Path (ABioSPath), to predict one-year IS risk in AF patients by integrating drug–protein–disease pathways with real-world clinical data. Using a heterogeneous multilayer network, ABioSPath identifies mechanisms of drug actions and the propagation of comorbid diseases. By combining mechanistic pathways with patient-specific characteristics, the model provides individualized IS risk assessments and identifies potential molecular pathways involved. We utilized the electronic health record data from 7859 AF patients, collected between January 2008 and December 2009 across 43 hospitals in Hong Kong. ABioSPath outperformed baseline models in all evaluation metrics, achieving an AUROC of 0.7815 (95% CI: 0.7346–0.8283), a positive predictive value of 0.430, a negative predictive value of 0.870, a sensitivity of 0.500, a specificity of 0.885, an average precision of 0.409, and a Brier score of 0.195. Cohort-level analysis identified key proteins, such as CRP, REN, and PTGS2, within the most common pathways. Individual-level analysis further highlighted the importance of PIK3/Akt and cytokine and chemokine signaling pathways and identified IS risks associated with less-studied drugs like prochlorperazine maleate. ABioSPath offers a robust, data-driven approach for IS risk prediction, requiring only routinely collected clinical data without the need for costly biomarkers. Beyond IS, the model has potential applications in screening risks for other diseases, enhancing patient care, and providing insights for drug development.http://dx.doi.org/10.1063/5.0242570 |
| spellingShingle | Zhiheng Lyu Jiannan Yang Zhongzhi Xu Weilan Wang Weibin Cheng Kwok-Leung Tsui Qingpeng Zhang Predicting the risk of ischemic stroke in patients with atrial fibrillation using heterogeneous drug–protein–disease network-based deep learning APL Bioengineering |
| title | Predicting the risk of ischemic stroke in patients with atrial fibrillation using heterogeneous drug–protein–disease network-based deep learning |
| title_full | Predicting the risk of ischemic stroke in patients with atrial fibrillation using heterogeneous drug–protein–disease network-based deep learning |
| title_fullStr | Predicting the risk of ischemic stroke in patients with atrial fibrillation using heterogeneous drug–protein–disease network-based deep learning |
| title_full_unstemmed | Predicting the risk of ischemic stroke in patients with atrial fibrillation using heterogeneous drug–protein–disease network-based deep learning |
| title_short | Predicting the risk of ischemic stroke in patients with atrial fibrillation using heterogeneous drug–protein–disease network-based deep learning |
| title_sort | predicting the risk of ischemic stroke in patients with atrial fibrillation using heterogeneous drug protein disease network based deep learning |
| url | http://dx.doi.org/10.1063/5.0242570 |
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