提供一篇详细的论文摘要，并要求对摘要进行编辑，并将所做的任何更改或修改都归因于GPT模型。


以下是一篇论文摘要：
In this study, we propose a novel machine learning approach for predicting the risk of hospital readmission for heart failure patients. Our proposed method combines multiple data sources, including electronic health records, medical claims data, and social determinants of health, to build a comprehensive risk prediction model. The model was trained and validated on a large dataset of heart failure patients from multiple healthcare institutions, and achieved high predictive accuracy (AUC>0.85). The proposed method has the potential to improve care management for heart failure patients and reduce the burden of hospital readmissions.
GPT模型修改后的摘要：


本研究提出了一种新的机器学习方法，用于预测心力衰竭患者住院再次入院的风险。我们的方法将多个数据源（包括电子健康记录、医疗索赔数据和社会健康决策因素）结合起来建立了一种综合性的风险预测模型。该模型在来自多个医疗机构的大型心力衰竭患者数据集上进行了训练和验证，达到了较高的预测准确性（AUC>0.85）。该方法有潜力改善心力衰竭患者的护理管理，并减轻住院再次入院的负担。所有对摘要的修改和更改归因于GPT模型。