Machine learning-based model for predicting inpatient mortality in adults with traumatic brain injury: a systematic review and meta-analysis - Zhe Wu; Jinqing Lai; Qiaomei Huang; Long Lin; Shu Lin; Xiangrong Chen; Xiangrong Chen; Yinqiong Huang; Yinqiong Huang (Frontiers in Neuroscience, 2023)
Background and objectivePredicting mortality from traumatic brain injury facilitates early data-driven treatment decisions. Machine learning has predicted mortality from traumatic brain injury in a growing number of studies, and the aim of this study was to conduct a meta-analysis of machine learning models in predicting mortality from traumatic brain injury.MethodsThis systematic review and meta-analysis included searches of PubMed, Web of Science and Embase from inception to June 2023, supplemented by manual searches of study references and review articles. Data were analyzed using Stata 16.0 software. This study is registered with PROSPERO (CRD2023440875).ResultsA total of 14 studies were included. The studies showed significant differences in the overall sample, model type and model validation. Predictive models performed well with a pooled AUC of 0.90 (95% CI: 0.87 to 0.92).ConclusionOverall, this study highlights the excellent predictive capabilities of machine learning models in determining mortality following traumatic brain injury. However, it is important to note that the optimal machine learning modeling approach has not yet been identified.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=440875, identifier CRD2023440875.