Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study
Abstract Background To develop a machine learning model for predicting acute respiratory distress syndrome (ARDS) events through commonly available parameters, including baseline characteristics and clinical and laboratory parameters. Methods A secondary analysis of a multi-centre prospective observ...
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doaj-b5cf9e7eeba147cd9eb6a11c7226c0bd2020-11-25T03:57:02ZengBMCJournal of Translational Medicine1479-58762019-10-0117111010.1186/s12967-019-2075-0Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort studyXian-Fei Ding0Jin-Bo Li1Huo-Yan Liang2Zong-Yu Wang3Ting-Ting Jiao4Zhuang Liu5Liang Yi6Wei-Shuai Bian7Shu-Peng Wang8Xi Zhu9Tong-Wen Sun10Department of General ICU, The First Affiliated Hospital of Zhengzhou University, Henan Key Laboratory of Critical Care MedicineDepartment of General ICU, The First Affiliated Hospital of Zhengzhou University, Henan Key Laboratory of Critical Care MedicineDepartment of General ICU, The First Affiliated Hospital of Zhengzhou University, Henan Key Laboratory of Critical Care MedicineDepartment of Critical Care Medicine, Peking University Third HospitalDepartment of General ICU, The First Affiliated Hospital of Zhengzhou University, Henan Key Laboratory of Critical Care MedicineIntensive Care Unit, Beijing Friendship Hospital Affiliated with Capital Medical UniversityIntensive Care Unit, Xiyuan Hospital Affiliated with the China Academy of Chinese Medical SciencesIntensive Care Unit, Beijing Shijitan Hospital Affiliated with Capital Medical UniversityIntensive Care Unit, China-Japan Friendship HospitalDepartment of Critical Care Medicine, Peking University Third HospitalDepartment of General ICU, The First Affiliated Hospital of Zhengzhou University, Henan Key Laboratory of Critical Care MedicineAbstract Background To develop a machine learning model for predicting acute respiratory distress syndrome (ARDS) events through commonly available parameters, including baseline characteristics and clinical and laboratory parameters. Methods A secondary analysis of a multi-centre prospective observational cohort study from five hospitals in Beijing, China, was conducted from January 1, 2011, to August 31, 2014. A total of 296 patients at risk for developing ARDS admitted to medical intensive care units (ICUs) were included. We applied a random forest approach to identify the best set of predictors out of 42 variables measured on day 1 of admission. Results All patients were randomly divided into training (80%) and testing (20%) sets. Additionally, these patients were followed daily and assessed according to the Berlin definition. The model obtained an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.82 and yielded a predictive accuracy of 83%. For the first time, four new biomarkers were included in the model: decreased minimum haematocrit, glucose, and sodium and increased minimum white blood cell (WBC) count. Conclusions This newly established machine learning-based model shows good predictive ability in Chinese patients with ARDS. External validation studies are necessary to confirm the generalisability of our approach across populations and treatment practices.http://link.springer.com/article/10.1186/s12967-019-2075-0Acute respiratory distress syndromeMachine learningPredictive model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xian-Fei Ding Jin-Bo Li Huo-Yan Liang Zong-Yu Wang Ting-Ting Jiao Zhuang Liu Liang Yi Wei-Shuai Bian Shu-Peng Wang Xi Zhu Tong-Wen Sun |
spellingShingle |
Xian-Fei Ding Jin-Bo Li Huo-Yan Liang Zong-Yu Wang Ting-Ting Jiao Zhuang Liu Liang Yi Wei-Shuai Bian Shu-Peng Wang Xi Zhu Tong-Wen Sun Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study Journal of Translational Medicine Acute respiratory distress syndrome Machine learning Predictive model |
author_facet |
Xian-Fei Ding Jin-Bo Li Huo-Yan Liang Zong-Yu Wang Ting-Ting Jiao Zhuang Liu Liang Yi Wei-Shuai Bian Shu-Peng Wang Xi Zhu Tong-Wen Sun |
author_sort |
Xian-Fei Ding |
title |
Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study |
title_short |
Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study |
title_full |
Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study |
title_fullStr |
Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study |
title_full_unstemmed |
Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study |
title_sort |
predictive model for acute respiratory distress syndrome events in icu patients in china using machine learning algorithms: a secondary analysis of a cohort study |
publisher |
BMC |
series |
Journal of Translational Medicine |
issn |
1479-5876 |
publishDate |
2019-10-01 |
description |
Abstract Background To develop a machine learning model for predicting acute respiratory distress syndrome (ARDS) events through commonly available parameters, including baseline characteristics and clinical and laboratory parameters. Methods A secondary analysis of a multi-centre prospective observational cohort study from five hospitals in Beijing, China, was conducted from January 1, 2011, to August 31, 2014. A total of 296 patients at risk for developing ARDS admitted to medical intensive care units (ICUs) were included. We applied a random forest approach to identify the best set of predictors out of 42 variables measured on day 1 of admission. Results All patients were randomly divided into training (80%) and testing (20%) sets. Additionally, these patients were followed daily and assessed according to the Berlin definition. The model obtained an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.82 and yielded a predictive accuracy of 83%. For the first time, four new biomarkers were included in the model: decreased minimum haematocrit, glucose, and sodium and increased minimum white blood cell (WBC) count. Conclusions This newly established machine learning-based model shows good predictive ability in Chinese patients with ARDS. External validation studies are necessary to confirm the generalisability of our approach across populations and treatment practices. |
topic |
Acute respiratory distress syndrome Machine learning Predictive model |
url |
http://link.springer.com/article/10.1186/s12967-019-2075-0 |
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