人工智能
计算机科学
机器学习
任务(项目管理)
疾病
图形
强化学习
对象(语法)
随机游动
医学
数学
理论计算机科学
统计
管理
病理
经济
作者
Zhoujian Sun,Wei Dong,Jinlong Shi,Zhengxing Huang
出处
期刊:IEEE transactions on systems, man, and cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2023-11-30
卷期号:54 (3): 1948-1959
标识
DOI:10.1109/tsmc.2023.3331847
摘要
Objective: To combine medical knowledge and medical data to interpretably predict the risk of disease. Methods: We formulated the disease progression prediction task as a random walk along a knowledge graph (KG). Specifically, we build a KG to record relationships between diseases and risk factors according to validated medical knowledge. Then, an object walks along the KG. It starts walking at a patient entity, which connects the KG based on the patient’s current diseases or risk factors and stops at a disease entity representing the predicted disease. The trajectory generated by the object represents an interpretable disease progression path of the given patient. The dynamics of the object are controlled by a policy-based reinforcement learning module, which is trained by electronic health records (EHRs). Experiments: We utilized three real-world EHR datasets to evaluate the performance of our model. In the disease progression prediction task, our model achieves 0.743, 0.639, and 0.643 in terms of macro area under the curve (AUC) in predicting 53 circulation system diseases in the three datasets, respectively. This performance is comparable to medical research’s commonly used machine learning models. In qualitative analysis, our clinical collaborator reviewed the disease progression paths generated by our model and advocated their interpretability and reliability. Conclusion: Experimental results validate the proposed model in interpretably evaluating and optimizing disease progression prediction. Significance: Our work contributes to leveraging the potential of medical knowledge and medical data jointly for interpretable prediction tasks.
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