Cheng Hengliang, Li Shibo, Shen Tao, Li Weihua
School of Information Science and Engineering, Yunnan University, Kunming, China.
Department of Colorectal Surgery, the Third Affiliated Hospital of Kunming Medical University, Kunming, China.
Artif Intell Med. 2025 May;163:103098. doi: 10.1016/j.artmed.2025.103098. Epub 2025 Mar 4.
Diagnosis prediction predicts which diseases a patient is most likely to suffer from in the future based on their historical electronic health records. The time series model can better capture the temporal progression relationship of patient diseases, but ignores the semantic correlation between all diseases; in fact, multiple diseases that are often diagnosed at the same time reflect hidden patterns that are conducive to diagnosis, so predefined global disease co-occurrence graph can help the model understand disease relationships. But it may contain a lot of noise and ignore the semantic adaptation of the disease under the diagnosis target. To this end, we propose a graph-driven end-to-end framework, named Adaptive Disease Representation Learning (ADRL), obtain disease representation after learning complex disease relationships, and then use it to improve diagnosis prediction performance. This model introduces an adaptive mechanism to dynamically adjust and optimize disease relationships by performing self-supervised perturbations on a predefined global disease co-occurrence graph, thereby learning a global disease relationship graph that contains complex semantic association information between diseases. The computational burden of adaptive global disease graph can be further alleviated by the proposed SVD-based accelerator. Finally, experimental results on two real-world EHR datasets show that the proposed model outperforms existing models in diagnosis prediction.
诊断预测基于患者的历史电子健康记录预测患者未来最有可能患的疾病。时间序列模型可以更好地捕捉患者疾病的时间进展关系，但忽略了所有疾病之间的语义关联；事实上，经常同时被诊断出的多种疾病反映了有助于诊断的隐藏模式，因此预定义的全局疾病共现图可以帮助模型理解疾病关系。但它可能包含大量噪声，并且忽略了诊断目标下疾病的语义适应性。为此，我们提出了一个图驱动的端到端框架，名为自适应疾病表征学习（ADRL），在学习复杂的疾病关系后获得疾病表征，然后用它来提高诊断预测性能。该模型引入了一种自适应机制，通过在预定义的全局疾病共现图上执行自监督扰动来动态调整和优化疾病关系，从而学习到一个包含疾病之间复杂语义关联信息的全局疾病关系图。所提出的基于奇异值分解的加速器可以进一步减轻自适应全局疾病图的计算负担。最后，在两个真实世界的电子健康记录数据集上的实验结果表明，所提出的模型在诊断预测方面优于现有模型。
