Data_Sheet_1_Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.pdf

Data_Sheet_1_Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.pdf

Background<p>With machine learning (ML) carving a niche in diverse medical disciplines, its role in sepsis prediction, a condition where the ‘golden hour’ is critical, is of paramount interest. This study assesses the factors influencing the efficacy of ML models in sepsis prediction, aiming t...

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Bibliographic Details
Main Author: Mikhail Ya Yadgarov (18543769) (author)
Other Authors: Giovanni Landoni (504703) (author), Levan B. Berikashvili (18543760) (author), Petr A. Polyakov (18543766) (author), Kristina K. Kadantseva (19857978) (author), Anastasia V. Smirnova (18543763) (author), Ivan V. Kuznetsov (19857981) (author), Maria M. Shemetova (18543781) (author), Alexey A. Yakovlev (19857984) (author), Valery V. Likhvantsev (18543757) (author)
Published: 2024
Subjects:
Dermatology
Emergency Medicine
Gastroenterology and Hepatology
Geriatrics and Gerontology
Intensive Care
Medical Genetics (excl. Cancer Genetics)
Nephrology and Urology
Nuclear Medicine
Orthopaedics
Otorhinolaryngology
Pathology (excl. Oral Pathology)
Radiology and Organ Imaging
Foetal Development and Medicine
Obstetrics and Gynaecology
Family Care
Primary Health Care
Medical and Health Sciences not elsewhere classified
sepsis
machine learning
network meta-analysis
decision trees
neural networks
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