Improvement of APACHE II score system for disease severity based on XGBoost algorithm.
Background: Prognostication is an essential tool for risk adjustment and decision making in the intensive care units (ICUs). In order to improve patient outcomes, we have been trying to develop a more effective model than Acute Physiology and Chronic Health Evaluation (APACHE) II to measure the seve...
| Published in: | BMC Medical Informatics & Decision Making Vol. 21; no. 1; pp. 1 - 13 |
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| Main Authors: | , , |
| Format: | research Journal Article |
| Published: |
BioMed Central
8/6/2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151774118&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151774118 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 8/6/2021 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 151774118 151774118 NLM34362354 151774118 10.1186/s12911-021-01591-x NLM34362354 151774118 ppf: 1 ppct: 12 formats: tig: atl: Improvement of APACHE II score system for disease severity based on XGBoost algorithm. aug: au: Luo, Yan Wang, Zhiyu Wang, Cong affil: School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, 100876, Beijing, China sug: subj: Intensive Care Units Algorithms ROC Curve Hospital Mortality Severity of Illness Indices APACHE (Acute Physiology and Chronic Health Evaluation) Prognosis Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies ab: Background: Prognostication is an essential tool for risk adjustment and decision making in the intensive care units (ICUs). In order to improve patient outcomes, we have been trying to develop a more effective model than Acute Physiology and Chronic Health Evaluation (APACHE) II to measure the severity of the patients in ICUs. The aim of the present study was to provide a mortality prediction model for ICUs patients, and to assess its performance relative to prediction based on the APACHE II scoring system.Methods: We used the Medical Information Mart for Intensive Care version III (MIMIC-III) database to build our model. After comparing the APACHE II with 6 typical machine learning (ML) methods, the best performing model was screened for external validation on anther independent dataset. Performance measures were calculated using cross-validation to avoid making biased assessments. The primary outcome was hospital mortality. Finally, we used TreeSHAP algorithm to explain the variable relationships in the extreme gradient boosting algorithm (XGBoost) model.Results: We picked out 14 variables with 24,777 cases to form our basic data set. When the variables were the same as those contained in the APACHE II, the accuracy of XGBoost (accuracy: 0.858) was higher than that of APACHE II (accuracy: 0.742) and other algorithms. In addition, it exhibited better calibration properties than other methods, the result in the area under the ROC curve (AUC: 0.76). we then expand the variable set by adding five new variables to improve the performance of our model. The accuracy, precision, recall, F1, and AUC of the XGBoost model increased, and were still higher than other models (0.866, 0.853, 0.870, 0.845, and 0.81, respectively). On the external validation dataset, the AUC was 0.79 and calibration properties were good.Conclusions: As compared to conventional severity scores APACHE II, our XGBoost proposal offers improved performance for predicting hospital mortality in ICUs patients. Furthermore, the TreeSHAP can help to enhance the understanding of our model by providing detailed insights into the impact of different features on the disease risk. In sum, our model could help clinicians determine prognosis and improve patient outcomes. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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