Abstract
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.
Highlights
Vitrectomy, an ocular surgery performed to partially or completely remove the vitreous, is widely used to treat various ocular conditions, such as cloudy vitreous, vitreous haemorrhage, retinal detachment, and proliferative diabetic retinopathy [1,2,3]
E analysis of clinical research data is challenging due to the complexity involved: on one hand, data need to meet the constraints of analytical models; on the other hand, the data characteristics need to be retained as far as possible in order to simulate the clinical situation [20]
E ANN model does not restrict the distribution of data, allowing researchers to make full use of data information. e ANN model has strong fault tolerance, so it can be widely used in the fields of prediction and analysis [21]. e ANN model has better fit than the LR model. e identification of risk factors for vitrectomy-associated dry eye disease (DED) and the accurate prediction of secondary DED after vitrectomy by ML models will be helpful for clinical decision-making, as well as the management of patients who have undergone vitrectomy
Summary
Vitrectomy, an ocular surgery performed to partially or completely remove the vitreous, is widely used to treat various ocular conditions, such as cloudy vitreous, vitreous haemorrhage, retinal detachment, and proliferative diabetic retinopathy [1,2,3]. Using the demographic and clinical features of patients to predict risk for vitrectomy-related DED will facilitate decision-making in the management of vitrectomy patients and improve the relationship between doctors and patients. To the best of our knowledge, no previous study has investigated the prediction of risk for secondary DED in patients scheduled to undergo vitrectomy. The algorithm learns a target function from labelled training data. Several techniques have been developed for supervised learning, those used most widely in healthcare and medicine are logistic regression (LR) and artificial neural networks (ANNs) [9, 10]. We used logistic regression and an ANN to construct models for predicting the risk of secondary dry eye after vitrectomy. We evaluated the performance of these clinical prediction models in assessing the risk of secondary dry eye after vitrectomy in order to elucidate the mechanism of secondary dry eye after vitrectomy
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