machine learning models for predicting critical illness risk in hospitalized patients CORD-Papers-2021-10-25 (Version 1)

Title: Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia
Abstract: Background: To develop machine learning classifiers at admission for predicting which patients with coronavirus disease 2019 (COVID-19) who will progress to critical illness Methods: A total of 158 patients with laboratory-confirmed COVID-19 admitted to three designated hospitals between December 31, 2019 and March 31, 2020 were retrospectively collected 27 clinical and laboratory variables of COVID-19 patients were collected from the medical records A total of 201 quantitative CT features of COVID-19 pneumonia were extracted by using an artificial intelligence software The critically ill cases were defined according to the COVID-19 guidelines The least absolute shrinkage and selection operator (LASSO) logistic regression was used to select the predictors of critical illness from clinical and radiological features, respectively Accordingly, we developed clinical and radiological models using the following machine learning classifiers, including naive bayes (NB), linear regression (LR), random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), K-nearest neighbor (KNN), kernel support vector machine (k-SVM), and back propagation neural networks (BPNN) The combined model incorporating the selected clinical and radiological factors was also developed using the eight above-mentioned classifiers The predictive efficiency of the models is validated using a 5-fold cross-validation method The performance of the models was compared by the area under the receiver operating characteristic curve (AUC) Results: The mean age of all patients was 58 9+/-13 9 years and 89 (56 3%) were males 35 (22 2%) patients deteriorated to critical illness After LASSO analysis, four clinical features including lymphocyte percentage, lactic dehydrogenase, neutrophil count, and D-dimer and four quantitative CT features were selected The XGBoost-based clinical model yielded the highest AUC of 0 960 [95% confidence interval (CI): 0 913-1 000)] The XGBoost-based radiological model achieved an AUC of 0 890 (95% CI: 0 757-1 000) However, the predictive efficacy of XGBoost-based combined model was very close to that of the XGBoost-based clinical model, with an AUC of 0 955 (95% CI: 0 906-1 000) Conclusions: A XGBoost-based based clinical model on admission might be used as an effective tool to identify patients at high risk of critical illness
Published: 2021
Journal: Journal of Thoracic Disease
Author Name: Liu, Q
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_q
Author Name: Pang, B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/pang_b
Author Name: Li, H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/li_h
Author Name: Zhang, B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhang_b
Author Name: Liu, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Lai, L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/lai_l
Author Name: Le, W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/le_w
Author Name: Li, J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/li_j
Author Name: Xia, T
Author link: https://covid19-data.nist.gov/pid/rest/local/author/xia_t
Author Name: Zhang, X
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhang_x
Author Name: Ou, C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/ou_c
Author Name: Ma, J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/ma_j
Author Name: Li, S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/li_s
Author Name: Guo, X
Author link: https://covid19-data.nist.gov/pid/rest/local/author/guo_x
Author Name: Zhang, S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhang_s
Author Name: Zhang, Q
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhang_q
Author Name: Jiang, M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jiang_m
Author Name: Zeng, Q
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zeng_q
license: unk
license_url: [unknown license]
source_x: WHO
source_x_url: https://www.who.int/
who_covidence_id: #1134641
has_full_text: FALSE
G_ID: machine_learning_models_for_predicting_critical_illness_risk_in_hospitalized_patients
S2 ID: 232230771