{"id":21820,"date":"2021-11-12T07:40:33","date_gmt":"2021-11-11T23:40:33","guid":{"rendered":"https:\/\/bmi.nycu.edu.tw\/?p=21820"},"modified":"2025-12-11T16:44:05","modified_gmt":"2025-12-11T08:44:05","slug":"emily-chia-yu-su","status":"publish","type":"post","link":"https:\/\/bmi.nycu.edu.tw\/en\/emily-chia-yu-su\/","title":{"rendered":"Emily Chia-Yu-Su"},"content":{"rendered":"\n\n\n\n\n\n\n\n
\n

Personal Profile<\/h3>\n<\/td>\n<\/tr>\n

\"\"<\/td>\nEmily Chia-Yu-Su<\/strong>
\nProfessor, Institute of Biomedical Informatics
\nPhD of Bioinformatics and Systems Biology, National Chiao Tung University, Taiwan
\nOffice: R306, 3F, SoRen Building
\nPhone: 886-2-28267391
\nEmail: emilysu@nycu.edu.tw
\nORCiD: 0000-0003-4801-5159<\/a><\/td>\n<\/tr>\n
\n

Research Interests<\/h3>\n<\/td>\n<\/tr>\n

\n
    \n
  • \u91ab\u5b78\u8cc7\u8a0a (Medical Informatics)<\/li>\n
  • \u6a5f\u5668\u5b78\u7fd2 (Machine Learning)<\/li>\n
  • \u81ea\u7136\u8a9e\u8a00 (Natural Language Processing)<\/li>\n
  • \u8a0a\u606f\u6d41\u884c\u75c5\u5b78 (Infodemiology Text Mining)<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Publications<\/h3>\n<\/td>\n<\/tr>\n

\n
    \n
  • \n
      \n
    • Journal Papers<\/strong>\n
        \n
      • \n
          \n
        • Chen YT#, Su ECY#<\/strong>, Hung FM, Hiramatsu T, Hung TZ and Kuo CY (2023, May). Constructing a Learning Curve to Discuss the Medical Treatments and the Effect of Vaccination of COVID-19. Healthcare, 11(11), 1591-1604.<\/li>\n
        • Chung CC, Su ECY<\/strong>, Chen JH, Chen YT and Kuo CY (2023, Feb). XGBoost-Based Simple Three-Item Model Accurately Predicts Outcomes of Acute Ischemic Stroke. Diagnostics, 13(5), 842-854.<\/li>\n
        • Su ECY<\/strong> and Wu HM (2023, Dec). Dimension reduction and visualization of multiple time series data: a symbolic data analysis approach. Computational Statistics (2023)<\/li>\n
        • Yuliana ME, Chou HC, Su ECY<\/strong>, Chuang HC, Huang LT and Chen CM (2023, Dec). Uteroplacental insufficiency decreases leptin expression and impairs lung development in growth-restricted newborn rats. Pediatric Research (2023)<\/li>\n
        • Vidyanti AN, Satiti S, Khairani AF, Fauzi AR, Hardhantyo M, Sufriyana H and Su ECY*<\/strong> (2023, Dec). Symptom-based scoring technique by machine learning to predict COVID-19: a validation study. BMC Infectious Diseases, 23:871-881<\/li>\n
        • Sufriyana H, Wu YW and Su ECY*<\/strong> (2023, May). Human-guided deep learning with ante-hoc explainability by convolutional network from non-image data for pregnancy prognostication. Neural Networks, 162:99-116.<\/li>\n
        • Husnayain A, Shim E, Fuad A and Su ECY*<\/strong> (2021, Dec). Predicting New Daily COVID-19 Cases and Deaths Utilizing Search Engine Query Data in South Korea from 2020 to 2021: Infodemiology Study. Journal of Medical Internet Research, 23(12):e34178-e34190.<\/li>\n
        • Husnayain A, Chuang TW, Fuad A and Su ECY*<\/strong> (2021, Jul). High variability in model performance of Google relative search volume in spatially clustered COVID-19 areas of the United States. International Journal of Infectious Diseases, 109, 269-278.<\/li>\n
        • Husnayain A, Shim E, Fuad A and Su ECY*<\/strong> (2020, Sep). Understanding the Community Risk Perceptions of the COVID-19 Outbreak in South Korea: Infodemiology Study. Journal of Medical Internet Research, 22(9):e19788-e19803.<\/li>\n
        • Sufriyana H, Wu YW and Su ECY*<\/strong> (2020, Apr). Artificial intelligence-assisted prediction of preeclampsia: development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia. EBioMedicine, 54:102710-102724.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

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