A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach

ASM Faizal, TM Thevarajah, SM Khor… - Computer methods and …, 2021 - Elsevier
Cardiovascular disease (CVD) is the leading cause of death worldwide and is a global
health issue. Traditionally, statistical models are used commonly in the risk prediction and …

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 …

Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations

D Doudesis, KK Lee, J Boeddinghaus, A Bularga… - Nature Medicine, 2023 - nature.com
Although guidelines recommend fixed cardiac troponin thresholds for the diagnosis of
myocardial infarction, troponin concentrations are influenced by age, sex, comorbidities and …

High-sensitivity cardiac troponin on presentation to rule out myocardial infarction: a stepped-wedge cluster randomized controlled trial

A Anand, KK Lee, AR Chapman, AV Ferry… - Circulation, 2021 - Am Heart Assoc
Background: High-sensitivity cardiac troponin assays enable myocardial infarction to be
ruled out earlier, but the safety and efficacy of this approach is uncertain. We investigated …

Routine laboratory blood tests predict SARS-CoV-2 infection using machine learning

HS Yang, Y Hou, LV Vasovic, PAD Steel… - Clinical …, 2020 - academic.oup.com
Background Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals
rapidly for management of patient care and protection of health care personnel are urgently …

Cardiac troponin thresholds and kinetics to differentiate myocardial injury and myocardial infarction

R Wereski, DM Kimenai, C Taggart, D Doudesis… - Circulation, 2021 - Am Heart Assoc
Background: Although the 99th percentile is the recommended diagnostic threshold for
myocardial infarction, some guidelines also advocate the use of higher troponin thresholds …

XGBoost, a novel explainable AI technique, in the prediction of myocardial infarction: a UK Biobank Cohort Study

A Moore, M Bell - Clinical Medicine Insights: Cardiology, 2022 - journals.sagepub.com
We wanted to assess if “Explainable AI” in the form of extreme gradient boosting (XGBoost)
could outperform traditional logistic regression in predicting myocardial infarction (MI) in a …

A review on joint carotid intima-media thickness and plaque area measurement in ultrasound for cardiovascular/stroke risk monitoring: artificial intelligence framework

M Biswas, L Saba, T Omerzu, AM Johri… - Journal of digital …, 2021 - Springer
Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide.
Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial …

Machine learning prediction of mortality in acute myocardial infarction

M Oliveira, J Seringa, FJ Pinto, R Henriques… - BMC Medical Informatics …, 2023 - Springer
Abstract Background Acute Myocardial Infarction (AMI) is the leading cause of death in
Portugal and globally. The present investigation created a model based on machine …

Medical diagnosis using machine learning: a statistical review

KA Bhavsar, J Singla, YD Al-Otaibi… - Computers …, 2021 - e-space.mmu.ac.uk
Decision making in case of medical diagnosis is a complicated process. A large number of
overlapping structures and cases, and distractions, tiredness, and limitations with the human …