Yang Wan-Ju, Wu Li, Mei Zhong-Ming, Xiang Yi
Department of Ophthalmology, The Central Hospital of Wuhan, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430014, Hubei Province, China.
J Ophthalmol. 2020 Apr 21;2020:1024926. doi: 10.1155/2020/1024926. eCollection 2020.
Supervised machine-learning (ML) models were employed to predict the occurrence of dry eye disease (DED) after vitrectomy in this study. The clinical data of 217 patients receiving vitrectomy from April 2017 to July 2018 were used as training dataset; the clinical data of 33 patients receiving vitrectomy from August 2018 to September 2018 were collected as validating dataset. The input features for ML training were selected based on the Delphi method and univariate logistic regression (LR). LR and artificial neural network (ANN) models were trained and subsequently used to predict the occurrence of DED in patients who underwent vitrectomy for the first time during the period. The area under the receiver operating characteristic curve (AUC-ROC) was used to evaluate the predictive accuracy of the ML models. The AUCs with use of the LR and ANN models were 0.741 and 0.786, respectively, suggesting satisfactory performance in predicting the occurrence of DED. When the two models were compared in terms of predictive power, the fitting effect of the ANN model was slightly superior to that of the LR model. In conclusion, both LR and ANN models may be used to accurately predict the occurrence of DED after vitrectomy.
在本研究中，采用监督式机器学习（ML）模型来预测玻璃体切除术后干眼症（DED）的发生情况。将2017年4月至2018年7月接受玻璃体切除术的217例患者的临床数据用作训练数据集；收集2018年8月至2018年9月接受玻璃体切除术的33例患者的临床数据作为验证数据集。基于德尔菲法和单因素逻辑回归（LR）选择用于ML训练的输入特征。对LR和人工神经网络（ANN）模型进行训练，随后用于预测在此期间首次接受玻璃体切除术的患者中DED的发生情况。采用受试者操作特征曲线下面积（AUC-ROC）来评估ML模型的预测准确性。使用LR和ANN模型时的AUC分别为0.741和0.786，表明在预测DED发生方面具有令人满意的性能。当比较这两种模型的预测能力时，ANN模型的拟合效果略优于LR模型。总之，LR和ANN模型均可用于准确预测玻璃体切除术后DED的发生情况。
