Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records
Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records
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DOI：
10.1016/j.ajo.2019.07.005
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发表时间：
2019-12-01
影响因子：
4.2
通讯作者：
Weinreb, Robert N.
中科院分区：
文献类型：
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作者：
Baxter, Sally L.;Marks, Charles;Weinreb, Robert N.
目得：使用电子健康记录（EHRs）中的系统数据预测原发性开角型青光眼（POAG）患者是否需要手术干预设计：机器学习模型的开发和评估方法：将来自单一学术机构的385例POAG患者的结构化EHR数据纳入多变量logistic回归，随机森林和人工神经网络模型。执行留一交叉验证。计算每个模型的平均受试者工作特征曲线下面积（AUC）、灵敏度、特异性、准确度和约登指数，以评价性能。系统变量驱动的预测进行了识别和解释。“结果：多变量logistic回归在区分需要手术的疾病进展患者方面最有效，AUC为0.67。平均收缩压升高与需要青光眼手术的几率显著增加相关（比值比[OR] = 1.09，P
PURPOSE: To predict the need for surgical intervention in patients with primary open-angle glaucoma (POAG) using systemic data in electronic health records (EHRs).DESIGN: Development and evaluation of machine learning models.METHODS: Structured EHR data of 385 POAG patients from a single academic institution were incorporated into models using multivariable logistic regression, random forests, and artificial neural networks. Leave one-out cross-validation was performed. Mean area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and Youden index were calculated for each model to evaluate performance. Systemic variables driving predictions were identified and interpreted. "RESULTS: Multivariable logistic regression was most effective at discriminating patients with progressive disease requiring surgery, with an AUC of 0.67. Higher mean systolic blood pressure was associated with significantly increased odds of needing glaucoma surgery (odds ratio [OR] = 1.09, P