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Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients

Background and Objectives: Early detection of sepsis is crucial and can save lives. However, identifying sepsis early and accurately remains a difficult task in the medical field. This study aims to investigate a new machine-learning approach. By analyzing the clinical laboratory results and vital s...

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Autores principales: Alanazi, Abdullah, Aldakhil, Lujain, Aldhoayan, Mohammed, Aldosari, Bakheet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385427/
https://www.ncbi.nlm.nih.gov/pubmed/37512087
http://dx.doi.org/10.3390/medicina59071276
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author Alanazi, Abdullah
Aldakhil, Lujain
Aldhoayan, Mohammed
Aldosari, Bakheet
author_facet Alanazi, Abdullah
Aldakhil, Lujain
Aldhoayan, Mohammed
Aldosari, Bakheet
author_sort Alanazi, Abdullah
collection PubMed
description Background and Objectives: Early detection of sepsis is crucial and can save lives. However, identifying sepsis early and accurately remains a difficult task in the medical field. This study aims to investigate a new machine-learning approach. By analyzing the clinical laboratory results and vital signs of adult patients in the ICU, this approach can predict and detect the initial signs of sepsis. Materials and Methods: To examine survival rates and predict outcomes, the study utilized several models, including the proportional hazards model and data mining algorithms. We analyzed data from the BESTCare database at KAMC, with a focus on patients aged 14 and older who were admitted to the ICU between April and October 2018. We conducted a thorough analysis of the medical records of a total of 1182 patients who were diagnosed with sepsis. Results: We studied two approaches to predict sepsis in ICU patients. The regression model utilizing survival analysis showed moderate predictive ability, emphasizing the importance of only three factors—time (from sepsis to an outcome; discharge or death), lactic acid, and temperature—had a significant p-value (p = 0.000568, p = 0.01, p = 0.02, respectively). Other data mining algorithms may have limitations due to their assumptions of variable independence and linear classification nature. Conclusions: To achieve progress and accuracy in the field of sepsis prediction, it is important to continuously strive for improvement. By meticulously cleaning and selecting data attributes, we can create a strong foundation for future advancements in this area.
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spelling pubmed-103854272023-07-30 Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients Alanazi, Abdullah Aldakhil, Lujain Aldhoayan, Mohammed Aldosari, Bakheet Medicina (Kaunas) Article Background and Objectives: Early detection of sepsis is crucial and can save lives. However, identifying sepsis early and accurately remains a difficult task in the medical field. This study aims to investigate a new machine-learning approach. By analyzing the clinical laboratory results and vital signs of adult patients in the ICU, this approach can predict and detect the initial signs of sepsis. Materials and Methods: To examine survival rates and predict outcomes, the study utilized several models, including the proportional hazards model and data mining algorithms. We analyzed data from the BESTCare database at KAMC, with a focus on patients aged 14 and older who were admitted to the ICU between April and October 2018. We conducted a thorough analysis of the medical records of a total of 1182 patients who were diagnosed with sepsis. Results: We studied two approaches to predict sepsis in ICU patients. The regression model utilizing survival analysis showed moderate predictive ability, emphasizing the importance of only three factors—time (from sepsis to an outcome; discharge or death), lactic acid, and temperature—had a significant p-value (p = 0.000568, p = 0.01, p = 0.02, respectively). Other data mining algorithms may have limitations due to their assumptions of variable independence and linear classification nature. Conclusions: To achieve progress and accuracy in the field of sepsis prediction, it is important to continuously strive for improvement. By meticulously cleaning and selecting data attributes, we can create a strong foundation for future advancements in this area. MDPI 2023-07-09 /pmc/articles/PMC10385427/ /pubmed/37512087 http://dx.doi.org/10.3390/medicina59071276 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alanazi, Abdullah
Aldakhil, Lujain
Aldhoayan, Mohammed
Aldosari, Bakheet
Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients
title Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients
title_full Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients
title_fullStr Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients
title_full_unstemmed Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients
title_short Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients
title_sort machine learning for early prediction of sepsis in intensive care unit (icu) patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385427/
https://www.ncbi.nlm.nih.gov/pubmed/37512087
http://dx.doi.org/10.3390/medicina59071276
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