用机器学习预测糖尿病患者急诊风险，提升早期干预能力。
Predicting Emergency Department Visits for Patients with Type II Diabetes
- 整合电子病历与社会决定因素数据，筛选87个关键特征建模。
- 随机森林与集成学习模型预测准确率最高，AUC达0.82。
- 识别出年龄、就诊间隔等核心风险因素，适合临床预警使用。
超过3000万美国人患有2型糖尿病（T2D），该疾病虽可治疗但存在重大健康风险。本研究旨在利用机器学习（ML）技术开发并验证预测模型，以估算T2D患者急诊科（ED）就诊风险。数据来自健康共享交换平台（HSX），涵盖人口统计、诊断记录和生命体征信息。样本包含34,151名确诊为T2D的患者，2017至2021年间共发生703,065次就诊。研究构建了融合电子病历（EMR）与社会决定健康因素（SDoH）的数据工作流，从2,555个特征中筛选出87个用于建模。采用多种机器学习算法（如CatBoost、集成学习、KNN、SVC、随机森林、XGBoost）结合十折交叉验证，预测患者是否面临急诊风险。各模型的ROC曲线下面积（AUC）分别为：随机森林0.82，XGBoost 0.82，集成学习0.82，CatBoost 0.81，KNN 0.72，SVC 0.68。集成学习与随机森林在区分度、校准性和临床适用性方面表现更优。这些模型可作为可靠工具，预测T2D患者急诊风险，辅助预估未来急诊需求，并帮助临床识别相关关键因素，实现早期干预以减少急诊就诊。前五大重要特征为：年龄、就诊间隔差值、就诊间隔、腹盆痛（R10）、收入极值指数（ICE）。
原文摘要 · Abstract (English)
Over 30 million Americans are affected by Type II diabetes (T2D), a treatable condition with significant health risks. This study aims to develop and validate predictive models using machine learning (ML) techniques to estimate emergency department (ED) visits among patients with T2D. Data for these patients was obtained from the HealthShare Exchange (HSX), focusing on demographic details, diagnoses, and vital signs. Our sample contained 34,151 patients diagnosed with T2D which resulted in 703,065 visits overall between 2017 and 2021. A workflow integrated EMR data with SDoH for ML predictions. A total of 87 out of 2,555 features were selected for model construction. Various machine learning algorithms, including CatBoost, Ensemble Learning, K-nearest Neighbors (KNN), Support Vector Classification (SVC), Random Forest, and Extreme Gradient Boosting (XGBoost), were employed with tenfold cross-validation to predict whether a patient is at risk of an ED visit. The ROC curves for Random Forest, XGBoost, Ensemble Learning, CatBoost, KNN, and SVC, were 0.82, 0.82, 0.82, 0.81, 0.72, 0.68, respectively. Ensemble Learning and Random Forest models demonstrated superior predictive performance in terms of discrimination, calibration, and clinical applicability. These models are reliable tools for predicting risk of ED visits among patients with T2D. They can estimate future ED demand and assist clinicians in identifying critical factors associated with ED utilization, enabling early interventions to reduce such visits. The top five important features were age, the difference between visitation gaps, visitation gaps, R10 or abdominal and pelvic pain, and the Index of Concentration at the Extremes (ICE) for income.
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