Shabbir Syed AbdulMARCO TULIO JONATHAN DIAZ PONCE2026-05-062026-05-062026-01-16https://203.71.86.71/handle/123456789/8966學位別：碩士 語文別：英文 口試委員：Shabbir Syed Abdul; 吳育瑋; 楊軒佳Background: Thyroid dysfunction, including hypothyroidism and hyperthyroidism, is a prevalent endocrine disorder that often goes undiagnosed due to non-specific symptoms and the absence of routine screening. Although thyroid-specific hormone testing is the diagnostic standard, emerging evidence suggests that routine blood test results may contain early biochemical signatures of thyroid imbalance, creating an opportunity for opportunistic screening using routinely collected laboratory data. Objective: This study aimed to develop and validate machine learning models capable of screening for thyroid dysfunction, excluding thyroid-specific hormones such as TSH, FT3, and FT4, using only routine blood test data in a real-world, multihospital clinical network setting. Methods: We conducted a retrospective observational study using anonymized patient data from the Taipei Medical University Clinical Research Database (TMUCRD), which includes records from Taipei Medical University Hospital, Wan Fang Hospital, and Shuang Ho Hospital. The source database spanned a 13- year period from January 2008 to December 2020. The final cohort included 11,887 patients, comprising 803 cases and 11,084 controls with cases classified as hypothyroidism (65.3%) or hyperthyroidism (34.7%). Models were trained using routine laboratory features and derived ratios within a sex- and age-matched case–control framework to mitigate class imbalance. To preserve clinical realism, missing laboratory measurements were not imputed, reflecting the fact that test availability depends on physician ordering decisions. Taking advantage of recent implementations of native missing-value handling in Random Forest models, this algorithm was included alongside XGBoost and LightGBM. Model performance was assessed using 5-fold stratified cross-validation during training and evaluated on a held-out test set using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. xiii Results: In the combined thyroid dysfunction cohort, the Random Forest model achieved the highest test performance, with an AUROC of 0.755, sensitivity of 0.820, and specificity of 0.575 at a screening-oriented operating point. For hypothyroidism, Random Forest also demonstrated the best performance, with a test AUROC of 0.765 and high sensitivity (0.857), supporting its suitability for screening applications. For hyperthyroidism, XGBoost achieved the highest discrimination, with a test AUROC of 0.749, sensitivity of 0.554, and specificity of 0.750. Across cohorts, feature importance analyses consistently highlighted renal, hematological, metabolic, and immune-related markers, including eGFR, creatinine, liver enzymes, lipid profiles, and white blood cell differentials. The inclusion of comorbidity indicators for diabetes mellitus, chronic kidney disease, and heart failure did not materially improve model performance, indicating that routinely available blood test parameters alone provide sufficient signal for screening-level identification of thyroid dysfunction.系所名稱：醫學資訊研究所碩士班thesis