Journal de santé et d'informatique médicale

Application of Machine and Deep Learning Algorithms in Intelligent Clinical Decision Support Systems in Healthcare

Abstract

Jong Taek Kim

Objective: The purpose of this paper is to review the PubMed/MEDLINE literature for articles that discuss the use of machine learning (ML) and deep learning (DL) for clinical decision support systems (CDSSs).

Materials and Methods: To identify relevant articles, we searched PubMed/MEDLINE through December 2nd, 2017. We identified a total of 283 studies.

Results: The number of ML and DL associated CDSS articles increased significantly beginning around 2010. The most common type of advanced artificial intelligence (AI) methodologies that the articles evaluated was neural networks also known as DL (n=109) followed by ML (n=86). The most common types of ML algorithm were support vector machines (n=78), logistic regression analysis (n=38), random forest (n=26), decision tree (n=25), and k-nearest neighbour (n=21). Cardiology, oncology, radiology, surgery, and critical care/ED were the most commonly represented specialties. Only 19 out of 283 (6.7%) ML and DL associated CDSS articles reported an effect on the process of care or patient outcomes.

Discussion: The current decade has seen research efforts and attention increase significantly in creating CDSS tools with the advanced AI methodologies of DL and ML. Although the research experiments demonstrate success, the scope of AI technology is still limited to a well-defined task. Also, most of these studies lack patient-oriented outcomes necessary to justify its widespread application in healthcare.

Conclusion: There is a clear upwards trend in ML and DL research in healthcare. However, in order to effectively translate successful AI research into the patient care, more clinically-relevant studies must be pursued.