Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis
Abstract Background Sepsis is a life-threatening condition caused by an abnormal response of the body to infection and imposes a significant health and economic burden worldwide due to its high mortality rate. Early recognition of sepsis is crucial for effective treatment. This study aimed to system...
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Format: | Article |
Language: | English |
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BMC
2023-09-01
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Series: | BMC Infectious Diseases |
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Online Access: | https://doi.org/10.1186/s12879-023-08614-0 |
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author | Zhenyu Yang Xiaoju Cui Zhe Song |
author_facet | Zhenyu Yang Xiaoju Cui Zhe Song |
author_sort | Zhenyu Yang |
collection | DOAJ |
description | Abstract Background Sepsis is a life-threatening condition caused by an abnormal response of the body to infection and imposes a significant health and economic burden worldwide due to its high mortality rate. Early recognition of sepsis is crucial for effective treatment. This study aimed to systematically evaluate the performance of various machine learning models in predicting the onset of sepsis. Methods We conducted a comprehensive search of the Cochrane Library, PubMed, Embase, and Web of Science databases, covering studies from database inception to November 14, 2022. We used the PROBAST tool to assess the risk of bias. We calculated the predictive performance for sepsis onset using the C-index and accuracy. We followed the PRISMA guidelines for this study. Results We included 23 eligible studies with a total of 4,314,145 patients and 26 different machine learning models. The most frequently used models in the studies were random forest (n = 9), extreme gradient boost (n = 7), and logistic regression (n = 6) models. The random forest (test set n = 9, acc = 0.911) and extreme gradient boost (test set n = 7, acc = 0.957) models were the most accurate based on our analysis of the predictive performance. In terms of the C-index outcome, the random forest (n = 6, acc = 0.79) and extreme gradient boost (n = 7, acc = 0.83) models showed the highest performance. Conclusion Machine learning has proven to be an effective tool for predicting sepsis at an early stage. However, to obtain more accurate results, additional machine learning methods are needed. In our research, we discovered that the XGBoost and random forest models exhibited the best predictive performance and were most frequently utilized for predicting the onset of sepsis. Trial registration CRD42022384015 |
first_indexed | 2024-03-10T22:14:25Z |
format | Article |
id | doaj.art-84b5da27d2454fcf8dc2b022c8b84bab |
institution | Directory Open Access Journal |
issn | 1471-2334 |
language | English |
last_indexed | 2024-03-10T22:14:25Z |
publishDate | 2023-09-01 |
publisher | BMC |
record_format | Article |
series | BMC Infectious Diseases |
spelling | doaj.art-84b5da27d2454fcf8dc2b022c8b84bab2023-11-19T12:29:02ZengBMCBMC Infectious Diseases1471-23342023-09-0123112110.1186/s12879-023-08614-0Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysisZhenyu Yang0Xiaoju Cui1Zhe Song2Kunming Medical UniversityChengyang District People’s HospitalQinghai UniversityAbstract Background Sepsis is a life-threatening condition caused by an abnormal response of the body to infection and imposes a significant health and economic burden worldwide due to its high mortality rate. Early recognition of sepsis is crucial for effective treatment. This study aimed to systematically evaluate the performance of various machine learning models in predicting the onset of sepsis. Methods We conducted a comprehensive search of the Cochrane Library, PubMed, Embase, and Web of Science databases, covering studies from database inception to November 14, 2022. We used the PROBAST tool to assess the risk of bias. We calculated the predictive performance for sepsis onset using the C-index and accuracy. We followed the PRISMA guidelines for this study. Results We included 23 eligible studies with a total of 4,314,145 patients and 26 different machine learning models. The most frequently used models in the studies were random forest (n = 9), extreme gradient boost (n = 7), and logistic regression (n = 6) models. The random forest (test set n = 9, acc = 0.911) and extreme gradient boost (test set n = 7, acc = 0.957) models were the most accurate based on our analysis of the predictive performance. In terms of the C-index outcome, the random forest (n = 6, acc = 0.79) and extreme gradient boost (n = 7, acc = 0.83) models showed the highest performance. Conclusion Machine learning has proven to be an effective tool for predicting sepsis at an early stage. However, to obtain more accurate results, additional machine learning methods are needed. In our research, we discovered that the XGBoost and random forest models exhibited the best predictive performance and were most frequently utilized for predicting the onset of sepsis. Trial registration CRD42022384015https://doi.org/10.1186/s12879-023-08614-0Machine learningSepsisIntensive care unitsMeta-analysis |
spellingShingle | Zhenyu Yang Xiaoju Cui Zhe Song Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis BMC Infectious Diseases Machine learning Sepsis Intensive care units Meta-analysis |
title | Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis |
title_full | Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis |
title_fullStr | Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis |
title_full_unstemmed | Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis |
title_short | Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis |
title_sort | predicting sepsis onset in icu using machine learning models a systematic review and meta analysis |
topic | Machine learning Sepsis Intensive care units Meta-analysis |
url | https://doi.org/10.1186/s12879-023-08614-0 |
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