DAPNet: multi-view graph contrastive network incorporating disease clinical and molecular associations for disease progression prediction
The proposed DAPNet, for the first time, utilizes comorbidity duration and disease associations network, enabling more accurate disease progression prediction based on a multi-view graph contrastive learning, which provides valuable insights for early diagnosis and treatment of patients. Based on disease association networks, our research has enhanced the interpretability of disease progression predictions.
Authors
- Zixin Shu (ORCID: https://orcid.org/0000-0001-6078-5645)
- Jian Yu (ORCID: https://orcid.org/0000-0002-9488-5182)
- Kuo Yang (ORCID: https://orcid.org/0000-0003-0736-4512)
- Xuezhong Zhou (ORCID: https://orcid.org/0000-0002-4713-3594)
- F Zhang
- Xiong He (ORCID: https://orcid.org/0000-0002-1501-2077)
- Yiming Liu (ORCID: https://orcid.org/0000-0001-7490-6508)
- Haoyu Tian (ORCID: https://orcid.org/0000-0003-3654-7836)
- Shihua Wang (ORCID: https://orcid.org/0000-0002-4958-3476)
- Baoyan Liu (ORCID: https://orcid.org/0000-0001-9075-4102)
- Jianan Xia (ORCID: https://orcid.org/0000-0002-4219-0602)
- Tiancai Wen (ORCID: https://orcid.org/0000-0002-7102-2210)
- Xinyu Dai (ORCID: https://orcid.org/0000-0002-4139-7337)
- Qiguang Zheng
Institutions
- Hebei Medical University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Beijing Jiaotong University (CN)
- Third Hospital of Hebei Medical University (CN)
- China Academy of Chinese Medical Sciences (CN)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2024-11-19
- DOI
- https://doi.org/10.1186/s12911-024-02756-0
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.35
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China