Utilizing machine learning algorithms for predicting Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI).

Journal: BMC psychiatry
PMID: 40102794

Abstract

BACKGROUND: Accurately diagnosing Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI) shows significant challenges as traditional diagnostic methods fail to meet expectations due to patient hesitance and non-psychiatric healthcare professionals' limitations. Therefore, the need for objective diagnostics highlights the potential of machine learning in identifying and treating ADCS-GI.

Authors

  • Min Tan
    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China.
  • Jinjin Zhao
    Key Laboratory of Evidence Science, Institute of Evidence Law and Forensic Science, China University of Political Science and Law, Ministry of Education, Beijing, China; Collaborative Innovation Center of Judicial Civilization, Beijing, China.
  • Yushun Tao
    Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Uroosa Sehar
    Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Yan Yan
    Department of Biomedical Engineering, Wayne State University, Detroit, Michigan, USA.
  • Qian Zou
    Department of Gastroenterology and Hepatology, Shenzhen University General Hospital, Shenzhen University, Shenzhen, 518055, China.
  • Qing Liu
    School of Chemistry and Chemical Engineering, Shandong University of Technology, 255049, Zibo, PR China.
  • Long Xu
    Solar Activity Prediction Center, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China.
  • Zeyang Xia
    Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, ShenZhen, China.
  • Lijuan Feng
    Shandong Institute of Pomology, Taian, Shandong, China.
  • Jing Xiong
    College of Computer Science, Sichuan Normal University, Chengdu, China.

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