Integration of Machine Learning Models with Clinical Decision Support Systems

EasyChair Preprint 13777

Abstract

The integration of machine learning (ML) models with clinical decision support systems (CDSS) holds great promise for revolutionizing healthcare decision-making. CDSS are computer-based tools that provide healthcare professionals with valuable information and recommendations to enhance patient care. ML models, on the other hand, excel in analyzing vast amounts of data and extracting meaningful insights. By combining ML capabilities with CDSS, healthcare providers can benefit from improved accuracy, efficiency, and personalized care.

This abstract provides an overview of the integration of ML models with CDSS, highlighting the benefits, challenges, and potential applications. It explores the various use cases where ML can enhance CDSS, such as diagnosis and risk prediction, treatment recommendation, real-time monitoring, and resource allocation optimization. Additionally, it emphasizes the importance of evaluating and validating integrated systems to ensure their reliability and effectiveness.

However, integrating ML models with CDSS is not without its challenges. Issues such as data availability, quality, and interoperability, as well as regulatory and ethical considerations, need to be addressed. User acceptance and adoption of these integrated systems also play a crucial role in their successful implementation.

Keyphrases: Personalized Care, enhance patient care, machine learning, support systems

@booklet{EasyChair:13777,
  author    = {Edwin Frank and Samon Daniel},
  title     = {Integration of Machine Learning Models with Clinical Decision Support Systems},
  howpublished = {EasyChair Preprint 13777},
  year      = {EasyChair, 2024}}