International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI-Assisted Emergency Triage System: A Rule-Based Clinical Decision Support Approach Using the Emergency Severity Index |
|---|---|
| ABSTRACT | Emergency Departments (EDs) globally face persistent challenges of patient overcrowding, delayed critical care, and resource misallocation. Traditional manual triage is time-consuming, subjective, and error-prone during peak operational hours. This paper presents the design and implementation of an AI-Assisted Emergency Triage System that automates patient severity classification using the Emergency Severity Index (ESI 1–5). The system collects multi-modal patient inputs — vital signs, demographics, chief complaint, AVPU consciousness level, pain level (0–10), and 25 curated clinical symptoms — processed through a rule-based weighted scoring engine. Outputs include ESI level, confidence score, AI reasoning log, vital sign assessments, and clinical recommendations, rendered through a responsive Next.js web dashboard. Evaluated against ESI clinical benchmarks, the system achieves 95% classification accuracy, delivers triage decisions 3× faster than manual processes, and projects a 40% reduction in ED wait times for critical patients. The approach demonstrates feasible, transparent, deployable AI decision support in emergency medicine without requiring large annotated datasets or complex model training. |
| AUTHOR | AMSAVENI P, JAMES SHIERLEY F, PRIYADHARSHINI D, RESHMA M, J REVATHY Department of Artificial Intelligence and Data Science, Christ The King Engineering College, Coimbatore, Tamil Nadu, India Head of Department & Project Guide, Department of Artificial Intelligence and Data Science, Christ The King Engineering College, Coimbatore, Tamil Nadu, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405036 |
| pdf/36_AI-Assisted Emergency Triage System A Rule-Based Clinical Decision Support Approach Using the Emergency Severity Index.pdf | |
| KEYWORDS | |
| References | [1] S. Alizadeh et al., 'Application of Artificial Intelligence in Triage in Emergencies and Disasters: A Systematic Review,' BMC Public Health, Springer Nature, 2024. DOI: 10.1186/s12889-024-20447-3. [2] M. Chowdhury et al., 'Medical Emergency Department Triage Data Processing Using a Machine Learning Solution,' Heliyon, Vol. 9, No. 4, 2023. [3] W.S. Hong et al., 'Predicting Hospital Admission at Emergency Department Triage Using Machine Learning,' PLOS ONE, Vol. 17, No. 7, 2019. [4] J. Kim et al., 'Interpretable Deep Learning System for Identifying Critical Patients Through the Prediction of Triage Level,' JMIR Medical Informatics, Vol. 12, 2024. [5] F. Ma et al., 'Deep Attention Model for Triage of Emergency Department Patients,' arXiv Preprint, arXiv: 1801.01228, 2018. [6] B. Mistry et al., 'Accuracy and Reliability of Emergency Department Triage Using the Emergency Severity Index,' Annals of Emergency Medicine, Vol. 71, No. 5, pp. 581–586, 2018. [7] Y. Raita et al., 'Emergency Department Triage Prediction of Clinical Outcomes Using Machine Learning Models,' Critical Care, Vol. 23, pp. 64, 2019. [8] A.A. Verma et al., 'Implementing Machine Learning in Medicine,' Canadian Medical Association Journal, Vol. 193, No. 34, 2021. |
