A Systematic Review of Medical Expert Systems for Cardiac Arrest Prediction

Abstract

Background:: Predicting cardiac arrest is crucial for timely intervention and improved patient outcomes. Machine learning has yielded astounding results by offering tailored prediction analyses on complex data. Despite advancements in medical expert systems, there remains a need for a comprehensive analysis of their effectiveness and limitations in cardiac arrest prediction. This need arises because there are not enough existing studies that thoroughly cover the topic. Objective:: The systematic review aims to analyze the existing literature on medical expert systems for cardiac arrest prediction, filling the gaps in knowledge and identifying key challenges. Methods:: This paper adopts the PRISMA methodology to conduct a systematic review of 37 publications obtained from PubMed, Springer, ScienceDirect, and IEEE, published within the last decade. Careful inclusion and exclusion criteria were applied during the selection process, resulting in a comprehensive analysis that utilizes five integrated layers- research objectives, data collection, feature set generation, model training and validation employing various machine learning techniques. Results and Conclusion:: The findings indicate that current studies frequently use ensemble and deep learning methods to improve machine learning predictions’ accuracy. However, they lack adequate implementation of proper pre-processing techniques. Further research is needed to address challenges related to external validation, implementation, and adoption of machine learning models in real clinical settings, as well as integrating machine learning with AI technologies like NLP. This review aims to be a valuable resource for both novice and experienced researchers, offering insights into current methods and potential future recommendations.