A Comprehensive Survey of Machine Learning and Deep Learning Methods for Clinical Decision Support Systems

Abstract

A Clinical Decision Support System (CDSS) is a type of health information technology that helps doctors and nurses make clinical decisions.  Health information technology (HIT), especially clinical decision support systems (CDSSs) that work with electronic health records (EHRs), can help people make more consistent judgments based on evidence.  There have been many published examples of CDSS success stories in the last ten years or more.  CDSS has evolved as an essential component in contemporary healthcare, thereby altering the method in which medical professionals make decisions, with the goals of enhancing clinical decision-making, improving patient outcomes, and optimizing healthcare delivery.  CDSSs can help in diagnosis, treatment, patient management, and prevention, which can lead to better patient outcomes, fewer medical errors, and better quality.  Clinicians generally use modern CDSS solutions at the point of care, where the system gives them data-driven insights and suggestions that help them improve their skills.  Priorities include investing in strong IT infrastructure, extensive training programs, and public awareness campaigns, as well as making CDSSs work better with EHR platforms.  India can use CDSSs to improve patient care and outcomes if government agencies, healthcare institutions, and technology suppliers work together to remove these impediments.  Overall, CDSS is a big step forward for smart healthcare systems.  It makes clinical workflow more efficient and helps patients get better results.  Finally, it is evaluated with two datasets from the UCI repository, and its performance is high in deep learning when compared with the other techniques in terms of accuracy, sensitivity, and specificity.