Abstract：
OBJECTIVE: Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications.
METHODS: A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient\'s EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions.
RESULTS: Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032).
CONCLUSIONS: These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions.
CONCLUSIONS: Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques.
METHODS: A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient\'s EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions.
RESULTS: Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032).
CONCLUSIONS: These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions.
CONCLUSIONS: Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques.
摘要：
目标：心力衰竭（HF）影响全球数百万患者，然而,治疗反应的可变性仍然是医疗保健专业人员面临的主要挑战.目前的治疗策略，主要来自基于人口的证据，往往没有考虑到患者个体的独特特征，导致次优结果。这项研究旨在开发特定于患者的计算模型，以预测治疗结果。通过利用大型电子健康记录(EHR)数据库。目标是通过识别可能从现有HF药物中受益的特定HF患者亚组来改善药物反应预测。
方法：小说，能够预测治疗反应的基于图的模型，结合图神经网络和变压器的发展。该方法通过将患者的EHR数据转换为图形结构而不同于常规方法。通过K-Means聚类定义基于这种表示的患者亚组，我们能够提高药物反应预测的性能.
结果：利用来自11.627MayoClinicHF患者的EHR数据，我们的模型在使用NT-proBNP作为5个药物类别的HF生物标志物预测药物反应方面显著优于传统模型(最佳RMSE为0.0043).确定了四个不同的患者亚组，具有不同的特征和结果，显示优于现有HF亚型的预测能力（最佳平均RMSE为0.0032）。
结论：这些结果突出了基于图形的EHR模型在改善HF治疗策略方面的功效。患者的分层揭示了可以从定制的反应预测中更显著受益的特定患者段。
结论：纵向EHR数据具有通过应用基于图形的AI技术来增强个性化预后预测的潜力。
方法：小说，能够预测治疗反应的基于图的模型，结合图神经网络和变压器的发展。该方法通过将患者的EHR数据转换为图形结构而不同于常规方法。通过K-Means聚类定义基于这种表示的患者亚组，我们能够提高药物反应预测的性能.
结果：利用来自11.627MayoClinicHF患者的EHR数据，我们的模型在使用NT-proBNP作为5个药物类别的HF生物标志物预测药物反应方面显著优于传统模型(最佳RMSE为0.0043).确定了四个不同的患者亚组，具有不同的特征和结果，显示优于现有HF亚型的预测能力（最佳平均RMSE为0.0032）。
结论：这些结果突出了基于图形的EHR模型在改善HF治疗策略方面的功效。患者的分层揭示了可以从定制的反应预测中更显著受益的特定患者段。
结论：纵向EHR数据具有通过应用基于图形的AI技术来增强个性化预后预测的潜力。
