Lin, Chin

Federated learning for predicting clinical outcomes in patients with COVID-19

Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., et al. (2021) · Nature Medicine

Machine Learning in HealthcareCOVID-19 diagnosis using AI

Cited by 842

Bone Marrow Fat and Bone Mineral Density on Proton MR Spectroscopy and Dual-Energy X-Ray Absorptiometry:Their Ratio as a New Indicator of Bone Weakening

Schellinger, D., Lin, C. S., Lim, J., Hatipoglu, H. G., Pezzullo, J. C., & Singer, A. J. (2004) · American Journal of Roentgenology

Bone health and osteoporosis researchAdvanced MRI Techniques and Applications

Cited by 132

AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial

Lin, C.-S., Liu, W.-T., Tsai, D.-J., Lou, Y.-S., Chang, C.-H., Lee, C.-C., Fang, W.-H., Wang, C.-C., Chen, Y.-Y., Lin, W.-S., et al. (2024) · Nature Medicine

ECG Monitoring and AnalysisMachine Learning in Healthcare

Cited by 115

A Deep-Learning Algorithm (ECG12Net) for Detecting Hypokalemia and Hyperkalemia by Electrocardiography: Algorithm Development

Lin, C.-S., Lin, C., Fang, W.-H., Hsu, C.-J., Chen, S.-J., Huang, K.-H., Lin, W.-S., Tsai, C.-S., Kuo, C.-C., Chau, T., et al. (2020) · JMIR Medical Informatics

ECG Monitoring and AnalysisMachine Learning in Healthcare

Cited by 109

Point-of-Care Artificial Intelligence-Enabled ECG for Dyskalemia: A Retrospective Cohort Analysis for Accuracy and Outcome Prediction

Lin, C., Chau, T., Lin, C.-S., Shang, H.-S., Fang, W.-H., Lee, D.-J., Lee, C.-C., Tsai, S.-H., Wang, C.-H., & Lin, S.-H. (2022) · npj Digital Medicine

ECG Monitoring and AnalysisPotassium and Related Disorders

Cited by 62

The Severity of Diabetic Retinopathy Is an Independent Factor for the Progression of Diabetic Nephropathy

Hsing, S.-C., Lee, C.-C., Lin, C., Chen, J.-T., Chen, Y.-H., & Fang, W.-H. (2020) · Journal of Clinical Medicine

Chronic Kidney Disease and DiabetesRetinal Diseases and Treatments

Cited by 35

Artificial intelligence-enabled electrocardiography from scientific research to clinical application

Lin, C.-S., Liu, W.-T., Chen, Y.-H., Lin, S.-H., & Lin, C. (2025) · EMBO Molecular Medicine

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare and Education

Cited by 15

Opportunistic Screening for Asymptomatic Left Ventricular Dysfunction With the Use of Electrocardiographic Artificial Intelligence: A Cost-Effectiveness Approach

Liu, W.-T., Hsieh, P.-H., Lin, C.-S., Fang, W.-H., Wang, C.-H., Tsai, C.-S., Hung, Y.-J., Hsieh, C.-B., Lin, C., & Tsai, D.-J. (2024) · Canadian Journal of Cardiology

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare

Cited by 15

Artificial Intelligence-Enabled Electrocardiography Identifies Severe Dyscalcemias and Has Prognostic Value

Lin, C., Chen, C.-C., Chau, T., Lin, C.-S., Tsai, S.-H., Lee, D.-J., Lee, C.-C., Shang, H.-S., & Lin, S.-H. (2022) · Clinica Chimica Acta

ECG Monitoring and AnalysisCardiac electrophysiology and arrhythmias

Cited by 10

Artificial Intelligence-Enabled Electrocardiography Contributes to Hyperthyroidism Detection and Outcome Prediction

Lin, C., Kuo, F.-C., Chau, T., Shih, J.-H., Lin, C.-S., Chen, C.-C., Lee, C.-C., & Lin, S.-H. (2024) · Communications Medicine

Thyroid Disorders and TreatmentsECG Monitoring and Analysis

Cited by 8