Improving sepsis classification performance with artificial intelligence algorithms: A comprehensive overview of healthcare applications.

Journal: Journal of critical care

Published Date: May 8, 2024

Abstract

PURPOSE: This study investigates the potential of machine learning (ML) algorithms in improving sepsis diagnosis and prediction, focusing on their relevance in healthcare decision-making. The primary objective is to contribute to healthcare decision-making by evaluating the performance of various supervised and unsupervised models.

Authors

Anjana G

Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India.
Nisha K L

Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India. Electronic address: nishaklshaji@gmail.com.
Arun Sankar M S

School of CS&IT, University College Cork, Cork, Ireland; Electronic and Communications Department, South East Technological University, Carlow, Ireland.

Keywords

Algorithms Artificial Intelligence Bayes Theorem Humans Machine Learning Sensitivity and Specificity Sepsis

External Resources

View on PubMed Access via DOI PubMed (38723336)

Improving sepsis classification performance with artificial intelligence algorithms: A comprehensive overview of healthcare applications.

Abstract

Authors

Keywords

External Resources

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