A diagnostic model for sepsis using an integrated machine learning framework approach and its therapeutic drug discovery.

Journal: BMC infectious diseases

PMID: 39953444

Abstract

BACKGROUND: Sepsis remains a life-threatening condition in intensive care units (ICU) with high morbidity and mortality rates. Some biomarkers commonly used in clinic do not have the characteristics of rapid and specific growth and rapid decline after effective treatment. Machine learning has shown great potential in early diagnosis, subtype analysis, accurate treatment and prognosis evaluation of sepsis.

Authors

Wuping Zhang

Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, No.152 Aiguo Road, Nanchang, Jiangxi Province, 330006, China.
Hanping Shi

Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China. Electronic address: shihp@ccmu.edu.cn.
Jie Peng

School of Physical Education, Liupanshui Normal University, Liupanshui, China.

Keywords

Algorithms Biomarkers Drug Discovery Gene Expression Profiling Gene Regulatory Networks Humans Machine Learning Nomograms Sepsis

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View on PubMed Access via DOI PubMed (39953444)

A diagnostic model for sepsis using an integrated machine learning framework approach and its therapeutic drug discovery.

Abstract

Authors

Keywords

External Resources

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