Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study.

Journal: BMC psychiatry
PMID: 39695446

Abstract

OBJECTIVE: Depression has emerged as a global public health concern with high incidence and disability rates, which are timely imperative to identify and intervene in clinical practice. The objective of this study was to explore the association between heart rate variability (HRV) and depression, with the aim of establishing and validating machine learning models for the auxiliary diagnosis of depression.

Authors

  • Min Yang
    College of Food Science and Engineering, Ocean University of China, Qingdao, 266003, Shandong, China.
  • Huiqin Zhang
    Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China.
  • Minglan Yu
    Institute of cardiovascular research, Southwest Medical University, No.1 Section 1, Xiang Lin Road, Longmatan District, Luzhou, 646000, P. R. China.
  • Yunxuan Xu
    School of Computer Science and Technology, Southwest University of Science and Technology, 59 Qinglong Road, Mianyang, 621010, P.R. China.
  • Bo Xiang
    Department of Pediatric Surgery, West China hospital, Sichuan University, No.37, Guoxue Alley, Chengdu, Sichuan, China. xb_scu.edu@hotmail.com.
  • Xiaopeng Yao
    School of Medical Information and Engineering, Southwest Medical University, Luzhou, China. xp_yao@swmu.edu.cn.

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