Using Random Forest Models to Identify Correlates of a Diabetic Peripheral Neuropathy Diagnosis from Electronic Health Record Data
Using Random Forest Models to Identify Correlates of a Diabetic Peripheral Neuropathy Diagnosis from Electronic Health Record Data
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DOI：
10.1093/pm/pnw096
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发表时间：
2017-01-01
期刊：
影响因子：
3.1
通讯作者：
Markman, John
中科院分区：
文献类型：
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作者：
DuBrava, Sarah;Mardekian, Jack;Markman, John
目标。将随机森林模型应用于电子健康记录，以确定与糖尿病周围神经病变(DPN)诊断相关的变量。回顾分析。设置。HUMEDICA识别电子健康记录数据库。研究对象为2008年1月1日至2013年9月30日的18岁2型糖尿病患者，其中35,050例有DPN前后1年的连续资料，288,328例无DPN的患者。人口统计、临床和卫生保健资源利用变量(例如，住院和门诊就诊、用药和手术)被输入随机森林模型，以确定DPN诊断的最重要的相关因素。随机森林建模是一种计算量大、健壮的数据挖掘技术，它容纳了大量的变量集，以使用分类树集合来识别相关因素。用受试者工作特征曲线(ROC)评价模型的准确性。最终的随机森林模型包括与DPN诊断相关的以下变量(重要性)：Charlson共病指数评分(100%)，年龄(37.1%)，指数前的手术和服务数量(29.7%)，指数前的门诊处方数(24.2%)，指数前的门诊就诊次数(18.3%)，指数前的实验室就诊次数(16.9%)，指数前的门诊就诊次数(12.1%)，住院患者的处方数量(5.9%)，与疼痛有关的药物处方数量(4.4%)。ROC分析证实了模型的有效性，曲线下面积为0.824，准确率为89.6%(95%可信区间为89.4%，89.8%)。随机森林建模可以确定DPN诊断的可能性。对随机森林模型的进一步验证可能有助于早期诊断和加强管理战略。
Objective. To identify variables correlated with a diagnosis of diabetic peripheral neuropathy (DPN) using random forest modeling applied to electronic health records.Design. Retrospective analysis.Setting. Humedica de-identified electronic health records database.Subjects. Subjects >= 18 years old with type 2 diabetes from January 1, 2008-September 30, 2013 having continuous data for 1 year pre- and post-index with DPN (n = 35,050) and without DPN (n = 288,328) were identified.Methods. Demographic, clinical, and health care resource utilization variables (e.g., inpatient and outpatient encounters, medications, and procedures) were input into a random forest model to identify the most important correlates of a DPN diagnosis. Random forest modeling is a computationally extensive, robust data mining technique that accommodates large sets of variables to identify associated factors using an ensemble of classifications trees. Accuracy of the model was evaluated using receiver operating characteristic curves (ROC).Results. The final random forest model consisted of the following variables (importance) associated with a DPN diagnosis: Charlson Comorbidity Index score (100%), age (37.1%), number of pre-index procedures and services (29.7%), number of pre-index outpatient prescriptions (24.2%), number of pre-index outpatient visits (18.3%), number of pre-index laboratory visits (16.9%), number of pre-index outpatient office visits (12.1%), number of inpatient prescriptions (5.9%), and number of pain-related medication prescriptions (4.4%). ROC analysis confirmed model performance, with an area under the curve of 0.824 and accuracy of 89.6% (95% confidence interval 89.4%, 89.8%).Conclusions. Random forest modeling can determine likelihood of a DPN diagnosis. Further validation of the random forest model may help facilitate earlier diagnosis and enhance management strategies.