Integrating Machine Learning in Clinical Decision Support Systems

Abstract

This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML algorithms, such as regression, random forest, and neural networks, the review aims to showcase their potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge, and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a collaborative approach to refine these systems for safety, efficacy, and equity.