The application of data mining, machine learning and artificial intelligence techniques in the field of diagnostics is not a new concept, and these techniques have been very successfully applied in a variety of applications, especially in dermatology and cancer research. But, in the case of medical problems that involve tests resulting in true or false (binary classification), the data generally has a class imbalance with samples majorly belonging to one class (ex: a patient undergoes a regular test and the results are false). Such disparity in data causes problems when trying to model predictive systems on the data. In critical applications like diagnostics, this class imbalance cannot be overlooked and must be given extra attention. In our research, we depict how we can handle this class imbalance through neural networks using a discriminative model and contrastive loss using a Siamese neural network structure. Such a model does not work on a probability-based approach to classify samples into labels. Instead it uses a distance-based approach to differentiate between samples classified under different labels. The code is available at https://tinyurl.com/DiscriminativeCHD/
Coronary Artery Disease Diagnosis; Ranking the Significant Features Using Random Trees Model
University of Birjand（比尔詹德大学）；Maragheh Faculty of Medical Sciences（马拉盖医科大学）；Queensland University of Technology（昆士兰科技大学）；Bauhaus Universität-Weimar（魏玛包豪斯大学）；Obuda University（奥布达大学）；Duy Tan University（越南大智大学）；Ton Duc Thang University（东德大学）
PUNCH: Physics-informed Uncertainty-aware Network for Coronary Hemodynamics
AngioInsight Inc.（AngioInsight公司）；University of California, Los Angeles（加州大学洛杉矶分校）；University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Michigan（密歇根大学）；Cardiovascular Research Foundation（心血管研究基金会）；University of California, Riverside（加州大学河滨分校）
Utilizing AI Language Models to Identify Prognostic Factors for Coronary Artery Disease: A Study in Mashhad Residents
University of Turin（都灵大学）；Golestan University of Medical Sciences（戈勒斯坦医科大学）；Macquarie University（麦考瑞大学）；Mashhad University of Medical Sciences（马什哈德医科大学）
Machine learning prediction of obstructive coronary artery disease using opportunistic coronary calcium and epicardial fat assessments from CT calcium scoring scans
Department of Biomedical Engineering, Virginia Commonwealth University（弗吉尼亚联邦大学生物医学工程系）；Department of Biomedical Engineering, Case Western Reserve University（凯斯西储大学生物医学工程系）；Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center（克利夫兰医学中心哈灵顿心脏和血管研究所）；BHF Centre for Cardiovascular Science, University of Edinburgh（爱丁堡大学BHF心血管科学中心）；Department of Radiology, Case Western Reserve University（凯斯西储大学放射学系）
Risk markers by sex for in-hospital mortality in patients with acute coronary syndrome: a machine learning approach
Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas（应用数学与系统研究所）；Instituto Mexicano del Seguro Social（墨西哥社会保险研究所）；Hospital Clínico Quirúrgico Hermanos Ameijeiras（阿梅赫拉斯兄弟临床外科医院）
A machine learning approach for Premature Coronary Artery Disease Diagnosis according to Different Ethnicities in Iran
Fasa University（法萨大学）；Deakin University（迪肯大学）；Queensland University of Technology（昆士兰科技大学）；Isfahan University of Medical Sciences（伊斯法罕医科大学）；University of Tabriz（大不里士大学）；Iran University of Medical Sciences（伊朗医科大学）；University of British Columbia（不列颠哥伦比亚大学）
Machine-Learning Identification of Hemodynamics in Coronary Arteries in the Presence of Stenosis
Babol Noshirvani University of Technology（巴博勒努希尔瓦尼技术大学）；Italian Institute of Technology（意大利技术研究院）；Royan Institute for Stem Cell Biology and Technology（罗扬干细胞生物学与技术研究所）；Sharif University of Technology（谢里夫理工大学）
Machine Learning-Based Model for Postoperative Stroke Prediction in Coronary Artery Disease
University of Southern California（南加州大学）；Iran University of Medical Sciences（伊朗医科大学）；California State University, Long Beach (CSULB)（加州州立大学长滩分校）；California State Polytechnic University, Pomona（加州州立理工大学波莫纳分校）
TabulaTime: A Novel Multimodal Deep Learning Framework for Advancing Acute Coronary Syndrome Prediction through Environmental and Clinical Data Integration
Department of Computing, and Mathematics, Manchester Metropolitan University（计算机与数学系，曼彻斯特 Metropolitan 大学）；Blackpool Teaching Hospitals NHS Foundation Trust（布莱克浦教学医院 NHS 基础信托）；Northern Care Alliance, NHS Foundation Trust（北方护理联盟，NHS 基础信托）；Department of Engineering, Manchester Metropolitan University（工程系，曼彻斯特 Metropolitan 大学）；Faculty of Business and Law, Manchester Metropolitan University（商业与法律学院，曼彻斯特 Metropolitan 大学）；Faculty of Medical Sciences, Newcastle University（医学科学学院，纽卡斯尔大学）
Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification
Chair for AI in Healthcare and Medicine, Technical University of Munich and TUM University Hospital（人工智能在医疗与医学中的研究所，慕尼黑技术大学及慕尼黑大学医院）；Department of Computing, Imperial College London（伦敦帝国理工学院计算机系）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML））；Department of Internal Medicine, TUM University Hospital（慕尼黑大学医院内科学系）
Predicting Lethal Outcome (Cause) And Understanding Key Biomarkers Linked With Acute Myocardial Infarction Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
