A bayesian network model for neurocognitive disorders digital screening in Chinese population: development and validation study.

BACKGROUND

Neurocognitive disorders (NCDs), classified under the ICD-10 codes F00-F09, are a category of mental disorders associated with brain disease, injury, or systemic conditions leading to cerebral dysfunction. NCDs represent a significant disease burden and an increasingly critical global public health challenge. Early screening for neurocognitive disorders is conducive to improving patients' quality of life and reducing healthcare costs. Therefore, there is an urgent need to develop an inexpensive and convenient screening model for neurocognitive disorders that can be applied to large populations to improve the efficiency of neurocognitive disorders screening.

METHODS

This study aimed to construct a classification model for screening neurocognitive disorders (NCDs) based on cross-sectional electronic health record data from the Cheeloo Whole Lifecycle eHealth Research-based Database (2015-2017). Eligible participants were adults aged 18 years or older, without prior diagnosis of neurocognitive disorders at baseline, covering multiple cities in Shandong Province, China. Among 1,626,817 individuals initially screened, 4,518 diagnosed NCD cases were included for model building and validation. Participants were assigned to a training set or a validation set based on their geographic locations. A Bayesian network classification model was developed by initially screening variables through univariate logistic regression. Gender and the top 30 variables with the highest coefficient of determination () in explaining the variance in NCD status were retained for model construction. Subsequently, the optimal network structure was identified using the Tabu search algorithm guided by Bayesian Information Criterion, with parameters estimated by maximum likelihood estimation. The model's performance was benchmarked against a multivariable logistic regression model. The model's performance was validated through ROC curves, calibration curves, and decision curves analysis. Sensitivity analyses were performed by introducing random missingness into the dataset to evaluate robustness of Bayesian network model and multivariable logistic regression model.

RESULTS

The final Bayesian network model included 31 variables in total, of which eight were directly connected to the neurocognitive disorders node in the learned Bayesian network structure. The Bayesian network model had good predictive discrimination, with AUC of 0.849 (95% CI; 0.839-0.859), 0.821 (95% CI; 0.803-0.840) and 0.800 (95% CI; 0.785-0.815) in the training, testing and validation sets, respectively. The calibration curves were well calibrated, and the decision curve analysis demonstrated its clinical applicability. In sensitivity analysis, the AUC of the Bayesian network model was 0.791 (95% CI; 0.777-0.806), with good robustness to missing data.

CONCLUSIONS

The findings of this study indicated that the established Bayesian network model could identify factors directly related to neurocognitive disorders and accurately predicted the risk of neurocognitive disorders in primary healthcare settings. The Bayesian network model is applicable to screening for neurocognitive disorders in large-scale electronic health record systems among adult populations. The proposed Bayesian network model incorporates 31 variables spanning demographic and clinical variables and demonstrates robustness to missing data, supporting its potential utility in clinical decision-making contexts.