Machine learning applications in healthcare clinical practice and research

Abstract

Machine learning (ML) is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis, thus creating machines that can complete tasks otherwise requiring human intelligence. Among its various applications, it has proven groundbreaking in healthcare as well, both in clinical practice and research. In this editorial, we succinctly introduce ML applications and present a study, featured in the latest issue of the World Journal of Clinical Cases. The authors of this study conducted an analysis using both multiple linear regression (MLR) and ML methods to investigate the significant factors that may impact the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease (NAFLD). Their results implicated age as the most important determining factor in both groups, followed by lactic dehydrogenase, uric acid, forced expiratory volume in one second, and albumin. In addition, for the NAFLD- group, the 5^th and 6^th most important impact factors were thyroid-stimulating hormone and systolic blood pressure, as compared to plasma calcium and body fat for the NAFLD+ group. However, the study's distinctive contribution lies in its adoption of ML methodologies, showcasing their superiority over traditional statistical approaches (herein MLR), thereby highlighting the potential of ML to represent an invaluable advanced adjunct tool in clinical practice and research.

Keywords: Machine; Learning; Artificial; Intelligence; Clinical; Practice; Research; Glomerular filtration rate; Non-alcoholic fatty liver disease; Medicine

Core Tip: Across numerous diverse industries, machine learning (ML) is revolutionizing healthcare as well. It has demonstrated the potential to aid in disease diagnosis, treatment planning, decision-making, and outcome prediction, as well as improve clinical trial design and their success rates, often surpassing traditional methods. We highlight a study, published in the World Journal of Clinical Cases, where ML techniques proved superior to traditional statistical methods in analyzing factors affecting the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease.