A novel Network Science Algorithm for Improving Triage of Patients

TL;DR

This paper introduces a new AI-based algorithm for patient triage that leverages patient data to improve accuracy, efficiency, and consistency in healthcare prioritization, outperforming traditional methods.

Contribution

It presents a novel machine learning algorithm trained on comprehensive patient data, demonstrating superior triage classification performance over existing approaches.

Findings

High accuracy in patient classification

Outperforms traditional triage methods

Potential to enhance healthcare efficiency

Abstract

Patient triage plays a crucial role in healthcare, ensuring timely and appropriate care based on the urgency of patient conditions. Traditional triage methods heavily rely on human judgment, which can be subjective and prone to errors. Recently, a growing interest has been in leveraging artificial intelligence (AI) to develop algorithms for triaging patients. This paper presents the development of a novel algorithm for triaging patients. It is based on the analysis of patient data to produce decisions regarding their prioritization. The algorithm was trained on a comprehensive data set containing relevant patient information, such as vital signs, symptoms, and medical history. The algorithm was designed to accurately classify patients into triage categories through rigorous preprocessing and feature engineering. Experimental results demonstrate that our algorithm achieved high accuracy and performance, outperforming traditional triage methods. By incorporating computer science into the triage process, healthcare professionals can benefit from improved efficiency, accuracy, and consistency, prioritizing patients effectively and optimizing resource allocation. Although further research is needed to address challenges such as biases in training data and model interpretability, the development of AI-based algorithms for triaging patients shows great promise in enhancing healthcare delivery and patient outcomes.