Predicting the Risk of Ischemic Stroke in Patients with Atrial Fibrillation using Heterogeneous Drug-protein-disease Network-based Deep Learning
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arXiv
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| Format: | Preprint |
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2024
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| author | Lyu, Zhiheng Yang, Jiannan Xu, Zhongzhi Wang, Weilan Cheng, Weibin Tsui, Kwok-Leung Tse, Gary Zhang, Qingpeng |
| author_facet | Lyu, Zhiheng Yang, Jiannan Xu, Zhongzhi Wang, Weilan Cheng, Weibin Tsui, Kwok-Leung Tse, Gary Zhang, Qingpeng |
| contents | We develop a deep learning model, ABioSPATH, to predict the one-year risk of ischemic stroke (IS) in atrial fibrillation (AF) patients. The model integrates drug-protein-disease pathways and real-world clinical data of AF patients to generate the IS risk and potential pathways for each patient. The model uses a multilayer network to identify the mechanism of drug action and disease comorbidity propagation pathways. The model is tested on the Electronic Health Record (EHR) data of 7859 AF patients from 43 hospitals in Hong Kong. The model outperforms all baselines across all metrics and provides valuable molecular-level insights for clinical use. The model also highlights key proteins in common pathways and potential IS risks tied to less-studied drugs. The model only requires routinely collected data, without requiring expensive biomarkers to be tested. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07475 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predicting the Risk of Ischemic Stroke in Patients with Atrial Fibrillation using Heterogeneous Drug-protein-disease Network-based Deep Learning Lyu, Zhiheng Yang, Jiannan Xu, Zhongzhi Wang, Weilan Cheng, Weibin Tsui, Kwok-Leung Tse, Gary Zhang, Qingpeng Quantitative Methods We develop a deep learning model, ABioSPATH, to predict the one-year risk of ischemic stroke (IS) in atrial fibrillation (AF) patients. The model integrates drug-protein-disease pathways and real-world clinical data of AF patients to generate the IS risk and potential pathways for each patient. The model uses a multilayer network to identify the mechanism of drug action and disease comorbidity propagation pathways. The model is tested on the Electronic Health Record (EHR) data of 7859 AF patients from 43 hospitals in Hong Kong. The model outperforms all baselines across all metrics and provides valuable molecular-level insights for clinical use. The model also highlights key proteins in common pathways and potential IS risks tied to less-studied drugs. The model only requires routinely collected data, without requiring expensive biomarkers to be tested. |
| title | Predicting the Risk of Ischemic Stroke in Patients with Atrial Fibrillation using Heterogeneous Drug-protein-disease Network-based Deep Learning |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2403.07475 |