The use of machine learning based models to predict the severity of community acquired pneumonia in hospitalised patients: A systematic review.

Abstract

Background: Community acquired pneumonia (CAP) is a common cause of hospital admission. CAP carries significant risk of adverse outcomes including organ dysfunction, intensive care unit (ICU) admission and death. Earlier admission to ICU for those with severe CAP is associated with better outcomes. Traditional prediction models are used in clinical practice to predict the severity of CAP. However, accuracy of predicting severity may be improved by using machine learning (ML) based models with added advantages of automation and speed. This systematic review evaluates the evidence base of ML-prediction tools in predicting CAP severity.

Methods: MEDLINE, EMBASE and PubMed were systematically searched for studies that used ML-based models to predict mortality and/or ICU admission in CAP patients, where a performance metric was reported.

Results: 11 papers including a total of 351,365 CAP patients were included. All papers predicted severity and four predicted ICU admission. Most papers applied multiple ML algorithms to datasets and derived area under the receiver operator characteristic curve (AUROC) of 0.98 at best performance and 0.57 at worst, with a mixed performance against traditional prediction tools.

Conclusion: Although ML models showed good performance at predicting CAP severity, the variables selected for inclusion in each model varied significantly which limited comparisons between models and there was a lack of reproducible data, limiting validity. Future research should focus on validating ML predication models in multiple cohorts to derive robust, reproducible performance measures, and to demonstrate a benefit in terms of patient outcomes and resource use.