Accurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients. The availability of electronic health records (EHR) has enabled machine learning advances in providing these predictions. However, many deep-learning-based methods are not satisfactory in solving several key challenges: 1) effectively utilizing disease domain knowledge; 2) collaboratively learning representations of patients and diseases; and 3) incorporating unstructured features. To address these issues, we propose a collaborative graph learning model to explore patient-disease interactions and medical domain knowledge. Our solution is able to capture structural features of both patients and diseases. The proposed modelmore »
This content will become publicly available on February 22, 2023
- Publication Date:
- NSF-PAR ID:
- 10318648
- Journal Name:
- In the Proceedings of the 36th AAAI Conference on Artificial Intelligence
- Sponsoring Org:
- National Science Foundation
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