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Clinical evidence

Every claim, with the study behind it

aiTriage™ comes from a research programme that began with heart rate variability in emergency department patients, has focused on chest pain since 2013, and now includes a pilot randomised controlled trial. Of the 19 publications below, 11 are in chest pain triage. Each links to its publication record.

How we validate

Research first, then regulation

Our algorithms are developed and validated with academic and clinical partners before they reach the product, and the product is built under an ISO 13485:2016 quality management system. Clearances and certificates are published on the regulatory page.

View regulatory documents

Clinical evidence

All publications

Filter by year, or browse the full list. Links open the publication record.

  • Pilot Evaluation of a Novel Artificial Intelligence (AI) Heart Rate Variability (HRV)-Guided Risk Stratification for Chest Pain in the Emergency Department: A Randomized Controlled Trial

    Authors: Wang Y, Li R, Pasupathi Y, Chan Y, Birthylon PI, Fook-Chong S, Zhang H, Lim SH, Kiat KTB, Basilio-Razon PI

    Journal: Clinical and Experimental Emergency Medicine · 2026

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  • Performance of a novel multimodal machine learning risk stratification tool (aiTriage v2.0) for chest pain patients presenting to the emergency department

    Authors: Wood B, Okada Y, Li R, Pasupathi Y, Chan MY, Birthylon P, Fook-Chong S, Zhang H, Lim SH, Tan K, Ong MEH, Koh ZX, Basilio-Razon PI

    Journal: Journal of the American College of Cardiology · 2026

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  • HRnV-Calc: A Software for Heart Rate n-Variability and Heart Rate Variability Analysis

    Authors: Niu C, Guo D, Ong MEH, Koh ZX, Guerry ALM, Ho AFW, Lin Z, Liu C, Clifford G, Liu N

    Journal: Journal of Open Source Software · 2023

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  • Adding heart rate n-variability (HRnV) to clinical assessment potentially improves prediction of serious bacterial infections in young febrile infants at the emergency department: a prospective observational study

    Authors: Chong S, Niu C, Piragasam R, Koh ZX, Guo D, Lee JH, Ong GY, Ong MEH, Liu N

    Journal: Annals of Translational Medicine · 2023

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  • Integrating Heart Rate Variability, Vital Signs, Electrocardiogram, and Troponin to Triage Chest Pain Patients in the ED

    Authors: Sakamoto JT, Liu N, Koh ZX, Guo D, Heldeweg MLA, Ng JCJ, Ong MEH

    Journal: The American Journal of Emergency Medicine · 2018

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  • Ensemble-Based Risk Scoring with Extreme Learning Machine for Prediction of Adverse Cardiac Events

    Authors: Liu N, Sakamoto JT, Cao J, Koh ZX, Ho AFW, Lin Z, Ong MEH

    Journal: Cognitive Computation · 2017

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  • Comparing HEART, TIMI and GRACE scores for prediction of 30-day major adverse cardiac events in high acuity chest pain patients in the emergency department

    Authors: Sakamoto JT, Liu N, Koh ZX, Fung NXJ, Heldeweg MLA, Ng JCJ, Ong MEH

    Journal: International Journal of Cardiology · 2016

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  • A novel cardiovascular risk stratification model incorporating ECG and heart rate variability for patients presenting to the emergency department with chest pain

    Authors: Heldeweg MLA, Liu N, Koh ZX, Fook-Chong S, Lye WK, Harms M, Ong MEH

    Journal: Critical Care · 2016

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  • Manifold ranking based scoring system with its application to cardiac arrest prediction: a retrospective study in emergency department patients

    Authors: Liu T, Lin Z, Ong MEH, Koh ZX, Pek PP, Yeo YK, Oh B, Ho AFW, Liu N

    Journal: Computers in Biology and Medicine · 2015

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  • Risk Stratification for Prediction of Adverse Coronary Events in Emergency Department Chest Pain Patients with a Machine Learning Score Compared with the TIMI Score

    Authors: Liu N, Lee MAB, Ho AFW, Haaland B, Fook-Chong S, Koh ZX, Pek PP, Chua ECP, Ting BP, Lin Z, Ong MEH

    Journal: International Journal of Cardiology · 2014

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  • Validation of a Risk Scoring Model for prediction of Acute Cardiac complications in Chest Pain Patients Presenting to the Emergency Department

    Authors: Liu N, Goh J, Lin Z, Koh ZX, Fook-Chong S, Haaland B, Wai KL, Ting BP, Shahidah N, Ong MEH

    Journal: International Journal of Cardiology · 2014

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  • Prediction of Adverse Cardiac Events in Emergency Department Patients with Chest Pain Using Machine Learning for Variable Selection

    Authors: Liu N, Koh ZX, Goh J, Lin Z, Haaland B, Ting BP, Ong MEH

    Journal: BMC Medical Informatics and Decision Making · 2014

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  • Risk Stratification with Extreme Learning Machine: A Retrospective Study on Emergency Department Patients

    Authors: Liu N, Cao J, Koh ZX, Pek PP, Ong MEH

    Journal: Mathematical Problems in Engineering · 2014

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  • Evolutionary Voting Based Extreme Learning Machines

    Authors: Liu N, Cao J, Lin Z, Pek PP, Koh ZX, Ong MEH

    Journal: Mathematical Problems in Engineering · 2014

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  • Risk Scoring for Prediction of Acute Cardiac Complications from Imbalanced Clinical Data

    Authors: Liu N, Koh ZX, Chua EC, Tan LM, Lin Z, Mirza B, Ong MEH

    Journal: IEEE Journal of Biomedical and Health Informatics · 2014

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  • Heart Rate Variability Risk Score for Prediction of Acute Cardiac Complications in ED Chest Pain Patients

    Authors: Ong MEH, Goh JY, Fook-Chong S, Haaland B, Khin LW, Koh ZX, Shahidah N, Lin Z

    Journal: The American Journal of Emergency Medicine · 2013

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  • An Intelligent Scoring System and Its Application to Cardiac Arrest Prediction

    Authors: Liu N, Lin Z, Cao J, Koh G, Zhang T, Ser W, Huang G, Ong MEH

    Journal: IEEE Transactions on Information Technology in Biomedicine · 2012

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  • Prediction of Cardiac Arrest in Critically Ill Patients presenting to the Emergency Department Using a Machine Learning Score Incorporating Heart Rate Variability Compared with the Modified Early Warning Score

    Authors: Ong MEH, Ng CHL, Goh KJY, Liu N, Koh ZX, Shahidah A, Zhang TT, Fook-Chong S, Lin Z

    Journal: Critical Care · 2012

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  • Patient Outcome Prediction with Heart Rate Variability and Vital Signs

    Authors: Liu N, Lin Z, Koh ZX, Huang GB, Ser W, Ong MEH

    Journal: Journal of Signal Processing Systems · 2010

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Discuss the evidence with our team

We are happy to walk through study design, results and how aiTriage was validated at each site.