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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Machine learning in the coagulation and hemostasis arena: an overview and evaluation of methods, review of literature, and future directions
Hooman H. Rashidi, Kelly Bowers, Morayma Reyes Gil
Journal of Thrombosis and Haemostasis (2023) Vol. 21, Iss. 4, pp. 728-743
Open Access | Times Cited: 21

Showing 21 citing articles:

Artificial intelligence and machine learning overview in pathology & laboratory medicine: A general review of data preprocessing and basic supervised concepts
Samer Albahra, Tom Gorbett, Scott Robertson, et al.
Seminars in Diagnostic Pathology (2023) Vol. 40, Iss. 2, pp. 71-87
Open Access | Times Cited: 76
Common statistical concepts in the supervised Machine Learning arena
Hooman H. Rashidi, Samer Albahra, Scott Robertson, et al.
Frontiers in Oncology (2023) Vol. 13
Open Access | Times Cited: 25
The ChatGPT conundrum: Human-generated scientific manuscripts misidentified as AI creations by AI text detection tool
Hooman H. Rashidi, Brandon D. Fennell, Samer Albahra, et al.
Journal of Pathology Informatics (2023) Vol. 14, pp. 100342-100342
Open Access | Times Cited: 20
Antibiotic combinations prediction based on machine learning to multicentre clinical data and drug interaction correlation
Jiaan Qin, Yuhe R. Yang, Chao Ai, et al.
International Journal of Antimicrobial Agents (2024) Vol. 63, Iss. 5, pp. 107122-107122
Closed Access | Times Cited: 5
Indirect comparison of two real-life studies in immune thrombocytopenia using artificial intelligence
Manuel Rodríguez-López, Raquel Ocampo-Martínez, Ramiro Núñez
(2025) Vol. 1, Iss. 1
Closed Access
Ten Machine Learning Models for Predicting Preoperative and Postoperative Coagulopathy in Patients With Trauma: Multicenter Cohort Study
Xiaojuan Xiong, Fu Hong, Bo Xu, et al.
Journal of Medical Internet Research (2025) Vol. 27, pp. e66612-e66612
Open Access
The future insights of AI Applications in Hematology diseases diagnosis and prognosis: Review Article
Hisham Ali Waggiallah, Abdulkareem Al-Garni, Aisha Ali M Ghazwani, et al.
Salud Ciencia y Tecnología (2025) Vol. 5, pp. 1430-1430
Closed Access
Artificial intelligence and machine learning in hemostasis and thrombosis
Paolo Gresele
Bleeding Thrombosis and Vascular Biology (2024) Vol. 2, Iss. 4
Open Access | Times Cited: 4
Advances in Platelet-Dysfunction Diagnostic Technologies
Inkwon Yoon, Jong Hyeok Han, Hee‐Jae Jeon
Biomolecules (2024) Vol. 14, Iss. 6, pp. 714-714
Open Access | Times Cited: 4
Introduction to Artificial Intelligence (AI) and Machine Learning (ML) in Pathology & Medicine: Generative & Non-Generative AI Basics
Hooman H. Rashidi, Joshua Pantanowitz, Matthew G Hanna, et al.
Modern Pathology (2025) Vol. 38, Iss. 4, pp. 100688-100688
Open Access
Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management
Shanshan Nie, Shan Zhang, Yuhang Zhao, et al.
Advances in Therapy (2024) Vol. 42, Iss. 2, pp. 636-665
Closed Access | Times Cited: 2
Machine learning-based Cerebral Venous Thrombosis diagnosis with clinical data
Ali Namjoo-Moghadam, Vida Abedi, Venkatesh Avula, et al.
Journal of Stroke and Cerebrovascular Diseases (2024) Vol. 33, Iss. 9, pp. 107848-107848
Closed Access | Times Cited: 1
Machine learning model for predicting physical activity related bleeding risk in Chinese boys with haemophilia A
Di Ai, Chang Cui, Yongqiang Tang, et al.
Thrombosis Research (2023) Vol. 232, pp. 43-53
Closed Access | Times Cited: 4
Artificial intelligence, science, and learning
David Lillicrap, James H. Morrissey
Journal of Thrombosis and Haemostasis (2023) Vol. 21, Iss. 4, pp. 709-709
Open Access | Times Cited: 3
Artificial intelligence for predicting pulmonary embolism: A review of machine learning approaches and performance evaluation
Ramón Puchades, Yale Tung‐Chen, Giorgina Salgueiro, et al.
Thrombosis Research (2023) Vol. 234, pp. 9-11
Closed Access | Times Cited: 2
Ten Machine Learning Models for Predicting Preoperative and Postoperative Coagulopathy in Patients With Trauma: Multicenter Cohort Study (Preprint)
Xiaojuan Xiong, Fu Hong, Bo Xu, et al.
(2024)
Closed Access
Venous thromboembolism in the era of machine learning and artificial intelligence in medicine
Morayma Reyes Gil, Joshua Pantanowitz, Hooman H. Rashidi
Thrombosis Research (2024) Vol. 242, pp. 109121-109121
Open Access
Innovative Diagnostic Solutions in Hemostasis
Emmanuel J. Favaloro, Leonardo Pasalic
Diagnostics (2024) Vol. 14, Iss. 22, pp. 2521-2521
Open Access
El juicio clínico: clave en la selección ante “el variado menú” de pruebas de laboratorio en hemostasia
Guillermo Andrés Herrera Rueda
Medicina y Laboratorio (2023) Vol. 27, Iss. 2, pp. 11-12
Open Access
Commentary: Optimizing transfusion and hemostasis practices in cardiac surgery: Human versus machine or human and machine?
Rashmi Nedadur, Dominique Vervoort
Journal of Thoracic and Cardiovascular Surgery (2023) Vol. 168, Iss. 4, pp. 1130-1131
Closed Access
EXPLORING THE INFLUENCE OF PHARMACOGENETIC VARIABILITY ON WARFARIN TREATMENT OUTCOMES
Mohammed Mana Mahdi Bani Humayyim, Mohammed Amer Ali Almutairi, Ibrahim Mudayni Alshuqaybi Alshuqaybi, et al.
Journal of Population Therapeutics and Clinical Pharmacology (2022)
Open Access
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