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Machine Learning (3060) Clinical Informatics (2154) Clinical Information and Decision Making (3548) Decision Support for Health Professionals (2126) Electronic Health Records (1663) Intensive Care Unit (ICU) (183)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/82230, first published .
Doctor and nurse check on patient in hospital room

Machine Learning–Augmented Traditional Analysis of Lactate vs Lactate-to-Albumin Ratio for Predicting Mortality Risk in Patients With Sepsis: Large-Scale Retrospective Study

Machine Learning–Augmented Traditional Analysis of Lactate vs Lactate-to-Albumin Ratio for Predicting Mortality Risk in Patients With Sepsis: Large-Scale Retrospective Study

Authors of this article:

Xiaodi Wang1 Author Orcid Image ;   Yi Xu2 Author Orcid Image ;   Weijun Xiao3 Author Orcid Image ;   Peng Dou2 Author Orcid Image ;   Yunxia Huang1 Author Orcid Image ;   Muhan Cao1 Author Orcid Image ;   Liang Luo4 Author Orcid Image ;   Qinghua Hou1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Xiaodi Wang   1 , PhD ;   Yi Xu   2 , PhD ;   Weijun Xiao   3 , MA ;   Peng Dou   2 , MA ;   Yunxia Huang   1 , MA ;   Muhan Cao   1 , MA ;   Liang Luo   4 , PhD ;   Qinghua Hou   1 , PhD

1 Department of Neurology, Clinical Neuroscience Center, the 7th Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong, China

2 Department of Rehabilitation medicine, the 7th Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong, China

3 Department of Epidemiology and Statistics, Clinical Medical Research Center, the 7th Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong, China

4 Department of Critical Care Medicine, the 7th Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong, China

Corresponding Author:

  • Qinghua Hou, PhD
  • Department of Neurology, Clinical Neuroscience Center, the 7th Affiliated Hospital, Sun Yat-Sen University
  • 628 Zhenyuan Road
  • Guangming
  • Shenzhen, Guangdong 518107
  • China
  • Phone: 86 18026280279
  • Email: houqh5@mail.sysu.edu.cn