Artificial Intelligence in Predicting Mortality Risk for Critically Ill Children in Pediatric Intensive Care Units: A Systematic Review

Background and Aims: The accurate prediction of mortality risk among critically ill children represents a major challenge in Pediatric Intensive Care Units (PICUs). Artificial Intelligence (AI) can find intricate patterns linked to mortality risk by examining large volumes of clinical data. This can help physicians better predict mortality risk and provide more efficient care. A systematic review can summarize the current evidence regarding the role of AI in predicting mortality among critically ill children in the PICU. Methods: A comprehensive and systematic search was conducted across three major electronic databases: PubMed, Scopus and Web of Science. The database searches were conducted on December 2, 2024. The screening process was conducted in two stages: title and abstract screening, and full-text review. This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Prediction model study Risk Of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and concerns regarding the applicability of the included studies. Results: Ultimately, the full text of 17 articles was reviewed for study evaluation and data extraction. 76% of the relevant research was conducted in 2020 or later, while 24% of the articles were published before 2020. The most frequently used algorithm in the studies was random forest. Overall, the Area Under the Receiver Operating Characteristic (AUROC) was greater than 0.8 in 88% of the studies and less than 0.8 in 12% of the studies. Conclusion: The studies emphasize the crucial role of machine learning and deep learning in improving mortality prediction in PICUs. The variability in AUROC values between different methods shows that while certain models excel in certain contexts, the choice of algorithm and feature selection significantly affect prediction accuracy.