Hepatitis carcinoma (HCC) accounts for the majority of liver cancer–related deaths globally. Cirrhosis often precedes HCC clinically in a strong, temporal relationship. Therefore, identifying cirrhosis patients at higher risk of HCC is crucial to physicians' clinical decision-making and patient management. Effective estimates of at-risk patients can facilitate timely therapeutic interventions and thereby enhance patient outcomes and well-being. We develop a novel, meta-path, attention-based deep learning method to identify at-risk cirrhosis patients. The proposed method integrates complex patient–medication interactions, essential patient–patient and medication–medication links, and the combined effects of medication and comorbidity to support downstream predictions. An empirical test of the proposed method's predictive utilities, relative to nine existing methods, uses a large sample of real-world cirrhosis patient data. The comparative results indicate that the proposed method can identify at-risk patients more effectively than all the benchmarks. The current research has important implications for clinical decision support and patient management, and it can facilitate patient self-management and treatment compliance too.

中文翻译：

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一种基于注意力的元路径深度学习方法，支持肝炎癌预测，从而改善肝硬化患者管理

肝炎癌 (HCC) 占全球肝癌相关死亡的大部分。临床上肝硬化通常先于 HCC，具有很强的时间关系。因此，识别肝癌风险较高的肝硬化患者对于医生的临床决策和患者管理至关重要。对高危患者的有效估计可以促进及时的治疗干预，从而改善患者的治疗效果和福祉。我们开发了一种新颖的、元路径的、基于注意力的深度学习方法来识别高危肝硬化患者。所提出的方法整合了复杂的患者与药物相互作用、基本的患者与患者和药物与药物之间的联系，以及药物和合并症的综合影响，以支持下游预测。相对于九种现有方法，对所提出的方法的预测效用进行了实证测试，使用了现实世界肝硬化患者数据的大样本。比较结果表明，所提出的方法比所有基准方法都能更有效地识别高危患者。目前的研究对临床决策支持和患者管理具有重要意义，也可以促进患者的自我管理和治疗依从性。