[HTML][HTML] Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review

J Stewart, J Lu, A Goudie, M Bennamoun, P Sprivulis… - PloS one, 2021 - journals.plos.org
Background Chest pain is amongst the most common reason for presentation to the
emergency department (ED). There are many causes of chest pain, and it is important for the …

[HTML][HTML] Utilizing machine learning dimensionality reduction for risk stratification of chest pain patients in the emergency department

N Liu, ML Chee, ZX Koh, SL Leow, AFW Ho… - BMC medical research …, 2021 - Springer
Background Chest pain is among the most common presenting complaints in the emergency
department (ED). Swift and accurate risk stratification of chest pain patients in the ED may …

A neural network aid for the early diagnosis of cardiac ischemia in patients presenting to the emergency department with chest pain

WG Baxt, FS Shofer, FD Sites, JE Hollander - Annals of emergency …, 2002 - Elsevier
Study objective: Chest pain is the second most common chief complaint presented to the
emergency department. Although the causes of chest pain span the clinical spectrum from …

[HTML][HTML] Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection

N Liu, ZX Koh, J Goh, Z Lin, B Haaland, BP Ting… - BMC medical informatics …, 2014 - Springer
Background The key aim of triage in chest pain patients is to identify those with high risk of
adverse cardiac events as they require intensive monitoring and early intervention. In this …

An artificial intelligence approach to early predict non-ST-elevation myocardial infarction patients with chest pain

CC Wu, WD Hsu, MM Islam, TN Poly, HC Yang… - Computer methods and …, 2019 - Elsevier
Abstract Background and Aims Hospital admission rate for the patients with chest pain has
already been increased worldwide but no existing risk score has been designed to stratify …

The Manchester Acute Coronary Syndromes (MACS) decision rule for suspected cardiac chest pain: derivation and external validation

R Body, S Carley, G McDowell, P Pemberton… - Heart, 2014 - heart.bmj.com
Objective We aimed to derive and validate a clinical decision rule (CDR) for suspected
cardiac chest pain in the emergency department (ED). Incorporating information available at …

Prehospital stratification in acute chest pain patient into high risk and low risk by emergency medical service: a prospective cohort study

K Wibring, M Lingman, J Herlitz, S Amin, A Bång - BMJ open, 2021 - bmjopen.bmj.com
Objectives To describe contemporary characteristics and diagnoses in prehospital patients
with chest pain and to identify factors suitable for the early recognition of high-risk and low …

[HTML][HTML] Chest pain in the emergency department: risk stratification with Manchester triage system and HEART score

L Leite, R Baptista, J Leitão, J Cochicho… - BMC cardiovascular …, 2015 - Springer
Background Fast and accurate chest pain risk stratification in the emergency department
(ED) is critical. The HEART score predicts the short-term incidence of major adverse cardiac …

Clinical value of diagnostic instruments for ruling out acute coronary syndrome in patients with chest pain: a systematic review

J Steurer, U Held, D Schmid, J Ruckstuhl… - Emergency Medicine …, 2010 - emj.bmj.com
Background Acute chest pain is a frequent reason to attend an emergency room, and
various instruments for calculating the probability of an acute coronary syndrome exist …

Artificial intelligence for diagnosis of acute coronary syndromes: a meta-analysis of machine learning approaches

PA Iannattone, X Zhao, J VanHouten, A Garg… - Canadian Journal of …, 2020 - Elsevier
Background Machine learning (ML) encompasses a wide variety of methods by which
artificial intelligence learns to perform tasks when exposed to data. Although detection of …