Hospital readmissions remain a challenge for healthcare systems, especially among patients with chronic conditions such as diabetes. Unplanned readmissions within 30 days are costly, strain hospital resources, and can indicate poor care coordination or discharge planning. In this work, we explore the use of machine learning to predict readmission risk for diabetic inpatients and propose a mixed reality (MR) to provide effective visualization and insights. We trained an XGBoost classifier after data cleaning, encoding, and feature engineering. The model achieved an Area Under the Receiver Operating characteristic Curve (AUROC) of 0.72 and an Area Under the Precision-Recall Curve (AUPRC) of 0.11. Key predictive factors included prior inpatient visits, discharge disposition, and glycemic control indicators such as A1C (blood sugar test) results and medication adjustments. Additionally, we developed an MR prototype that visualize patient records and predictions containing risk level, major contributing factors, and a concise summary of care. Together, the predictive model and the MR interface aim to improve clinician awareness and communication around readmission risk in real-time clinical settings.

翻译：医院再入院仍是医疗系统面临的挑战，尤其是糖尿病等慢性病患者。30天内的非计划再入院不仅成本高昂、占用医院资源，还反映出护理协调或出院规划的不完善。本研究探索利用机器学习预测糖尿病住院患者的再入院风险，并提出混合现实（MR）技术以实现有效的可视化和洞察。经过数据清洗、编码和特征工程后，我们训练了一个XGBoost分类器。该模型的受试者工作特征曲线下面积（AUROC）为0.72，精确率-召回率曲线下面积（AUPRC）为0.11。关键预测因素包括既往住院次数、出院去向以及血糖控制指标，如A1C（血糖测试）结果和药物调整情况。此外，我们开发了一个MR原型，可直观展示包含风险等级、主要影响因素和简明护理总结的患者记录与预测结果。预测模型与MR界面相结合，旨在实时临床环境中提升临床医生对再入院风险的认识和沟通效率。