International journal of medical informatics · 2026
Development and validation of data-driven, decision tree-based algorithms for identifying Behçet's disease in claims data.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Sada Ken-Ei, Miyawaki Yoshia, Yanai Ryo, Kida Takashi, Onishi Akira, Yoshimi Ryusuke, Ichinose Kunihiro, Shimojima Yasuhiro
Аннотация
OBJECTIVE: To develop and externally validate novel, data-driven algorithms that are based on appropriate variable selection methods for identifying patients with Behçet's disease in Japan.
METHODS: This retrospective cross-sectional study included 13,538 patients from six tertiary hospitals (November-December 2023). One year of claims data was linked to chart-confirmed Behçet's disease diagnoses. Patients were randomly divided into training (n = 8,811) and test (n = 3,775) sets, with external validation (n = 952) from another hospital. Feature selection among Behçet's disease-coded patients used the Least Absolute Shrinkage and Selection Operator, Boruta, and Recursive Feature Elimination. The diagnostic performance of the rule-based algorithms, which were derived from the decision tree models, was evaluated using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value, and F1 score.
RESULTS: Diagnosis codes alone achieved high sensitivity (1.000) and specificity (0.992) but modest PPV (0.767, test set; 0.850, external validation). Incorporating sulphamethoxazole-trimethoprim and colchicine prescriptions improved the positive predictive value, which was 0.793 in the test set and 0.865 in external validation.
CONCLUSION: Incorporating prescriptions alongside diagnosis codes improved PPV while maintaining high sensitivity and specificity. Building upon a data-driven framework that integrates variable selection methods and decision tree analysis, this study provides a validated and scalable approach for reliable claims-based research on Behçet's disease.
METHODS: This retrospective cross-sectional study included 13,538 patients from six tertiary hospitals (November-December 2023). One year of claims data was linked to chart-confirmed Behçet's disease diagnoses. Patients were randomly divided into training (n = 8,811) and test (n = 3,775) sets, with external validation (n = 952) from another hospital. Feature selection among Behçet's disease-coded patients used the Least Absolute Shrinkage and Selection Operator, Boruta, and Recursive Feature Elimination. The diagnostic performance of the rule-based algorithms, which were derived from the decision tree models, was evaluated using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value, and F1 score.
RESULTS: Diagnosis codes alone achieved high sensitivity (1.000) and specificity (0.992) but modest PPV (0.767, test set; 0.850, external validation). Incorporating sulphamethoxazole-trimethoprim and colchicine prescriptions improved the positive predictive value, which was 0.793 in the test set and 0.865 in external validation.
CONCLUSION: Incorporating prescriptions alongside diagnosis codes improved PPV while maintaining high sensitivity and specificity. Building upon a data-driven framework that integrates variable selection methods and decision tree analysis, this study provides a validated and scalable approach for reliable claims-based research on Behçet's disease.