533694Business School, Sichuan University, Chengdu, China.
439679West China School of Nursing, West China Hospital, Sichuan University, Chengdu, China.
Clin Appl Thromb Hemost. 2021 Jan-Dec;27:10760296211008650. doi: 10.1177/10760296211008650.
In order to overcome the shortage of the current costly DVT diagnosis and reduce the waste of valuable healthcare resources, we proposed a new diagnostic approach based on machine learning pre-test prediction models using EHRs. We examined the sociodemographic and clinical factors in the prediction of DVT with 518 NICU admitted patients, including 189 patients who eventually developed DVT. We used cross-validation on the training data to determine the optimal parameters, and finally, the applied ROC analysis is adopted to evaluate the predictive strength of each model. Two models (GLM and SVM) with the strongest ROC were selected for DVT prediction, based on which, we optimized the current intervention and diagnostic process of DVT and examined the performance of the proposed approach through simulations. The use of machine learning based pre-test prediction models can simplify and improve the intervention and diagnostic process of patients in NICU with suspected DVT, and reduce the valuable healthcare resource occupation/usage and medical costs.
为了克服当前昂贵的 DVT 诊断方法的局限性，减少宝贵医疗资源的浪费，我们提出了一种新的基于机器学习预测试预测模型的诊断方法，该方法利用了电子健康记录（EHR）。我们研究了 518 名入住新生儿重症监护病房（NICU）的患者的社会人口统计学和临床因素，以预测 DVT 的发生，其中 189 名患者最终发展为 DVT。我们在训练数据上使用交叉验证来确定最佳参数，最后，采用应用 ROC 分析来评估每个模型的预测强度。选择了两个具有最强 ROC 的模型（GLM 和 SVM）来进行 DVT 预测，在此基础上，我们优化了当前 DVT 的干预和诊断流程，并通过模拟来检验所提出方法的性能。基于机器学习的预测试预测模型的使用可以简化和改进疑似 DVT 的 NICU 患者的干预和诊断流程，并减少宝贵医疗资源的占用/使用和医疗费用。
