Department of Operations and Information Systems, David Eccles School of Business, University of Utah, Salt Lake City, Utah, USA.
Department of Information Systems, College of Business, California State University Long Beach, Long Beach, California, USA.
J Am Med Inform Assoc. 2023 Apr 19;30(5):846-858. doi: 10.1093/jamia/ocad008.
Estimating the deterioration paths of chronic hepatitis B (CHB) patients is critical for physicians' decisions and patient management. A novel, hierarchical multilabel graph attention-based method aims to predict patient deterioration paths more effectively. Applied to a CHB patient data set, it offers strong predictive utilities and clinical value.
The proposed method incorporates patients' responses to medications, diagnosis event sequences, and outcome dependencies to estimate deterioration paths. From the electronic health records maintained by a major healthcare organization in Taiwan, we collect clinical data about 177 959 patients diagnosed with hepatitis B virus infection. We use this sample to evaluate the proposed method's predictive efficacy relative to 9 existing methods, as measured by precision, recall, F-measure, and area under the curve (AUC).
We use 20% of the sample as holdouts to test each method's prediction performance. The results indicate that our method consistently and significantly outperforms all benchmark methods. It attains the highest AUC, with a 4.8% improvement over the best-performing benchmark, as well as 20.9% and 11.4% improvements in precision and F-measures, respectively. The comparative results demonstrate that our method is more effective for predicting CHB patients' deterioration paths than existing predictive methods.
The proposed method underscores the value of patient-medication interactions, temporal sequential patterns of distinct diagnosis, and patient outcome dependencies for capturing dynamics that underpin patient deterioration over time. Its efficacious estimates grant physicians a more holistic view of patient progressions and can enhance their clinical decision-making and patient management.
估计慢性乙型肝炎（CHB）患者的恶化路径对于医生的决策和患者管理至关重要。一种新颖的分层多标签图注意力方法旨在更有效地预测患者的恶化路径。将其应用于 CHB 患者数据集，可提供强大的预测效用和临床价值。
该方法整合了患者对药物的反应、诊断事件序列和结果依赖性，以估计恶化路径。从台湾一家主要医疗机构的电子健康记录中，我们收集了 177959 名乙型肝炎病毒感染患者的临床数据。我们使用该样本评估所提出的方法相对于 9 种现有方法的预测效果，以精度、召回率、F 值和曲线下面积（AUC）来衡量。
我们使用样本的 20%作为保留样本来测试每种方法的预测性能。结果表明，我们的方法始终显著优于所有基准方法。它获得了最高的 AUC，比表现最好的基准方法提高了 4.8%，精度和 F 值分别提高了 20.9%和 11.4%。比较结果表明，与现有预测方法相比，我们的方法在预测 CHB 患者的恶化路径方面更为有效。
该方法强调了患者-药物相互作用、不同诊断的时间序列模式以及患者结果依赖性在捕捉随时间推移而导致患者恶化的动态方面的价值。其有效的估计为医生提供了更全面的患者进展视图，并可以增强他们的临床决策和患者管理。
