Data Sheet 1_A TabPFN-based prediction system for refractive error and dry eye comorbidity: a retrospective study using large-scale real-world data.docx

IntroductionRefractive error and dry eye are highly prevalent ocular conditions that significantly impair the quality of life and impose a substantial burden on individuals and society. Growing evidence suggests a correlation between these two conditions. This study aimed to develop and validate a machine learning (ML) model to accurately predict the risk of concurrent dry eye comorbidities in patients with refractive error.
MethodsData from Xiamen Eye Center outpatient database (1st January 2024 to 28th February 2025) were analyzed (n = 114,579). Hyperparameter optimization, Spearman correlation analysis, and logistic regression analyses were performed. The final feature set was determined using a Random Forest algorithm with the sequential forward selection technique. Eight ML algorithms were evaluated through ten-fold cross-validation. The optimal model was selected based on a comprehensive assessment of the receiver operating characteristic curve, precision-recall curve, and decision curve analysis. For the best-performing model, SHapley Additive exPlanations and partial dependence plots were utilized to interpret the importance and interactions of risk factors.
ResultsBaseline characteristics were comparable between the training set and the internal test set, while significant differences were observed in multiple baseline characteristics between dry eye group and non-dry eye group among subjects with refractive error. Based on ten selected feature variables, the tabular prior-data fitted network (TabPFN) model demonstrated the best performance, showing high screening efficacy with both specificity and accuracy reaching 0.945. The interaction analysis revealed that a longer duration of refractive error was associated with a higher risk of dry eye, a relationship that was particularly pronounced among older and female patients. Furthermore, an online web calculator was developed to deploy this diagnostic prediction model.
DiscussionThis study developed a high-performance and interpretable ML system based on a large-scale real-world clinical dataset for the early prediction of concurrent dry eye risk in patients with refractive error. The system holds significant potential as a predictive aid for clinical decision-making, enabling more timely and personalized patient management, thereby offering substantial clinical value and promising application prospects.