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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:

Prehospital diagnostic algorithm for acute coronary syndrome using machine learning: a prospective observational study
Masahiko Takeda, Takehiko Oami, Yosuke Hayashi, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 16

Showing 16 citing articles:

International evaluation of an artificial intelligence–powered electrocardiogram model detecting acute coronary occlusion myocardial infarction
Robert Herman, H. Pendell Meyers, Stephen W. Smith, et al.
European Heart Journal - Digital Health (2023) Vol. 5, Iss. 2, pp. 123-133
Open Access | Times Cited: 52
Artificial intelligence in emergency medicine. A systematic literature review
Konstantin Piliuk, Sven Tomforde
International Journal of Medical Informatics (2023) Vol. 180, pp. 105274-105274
Open Access | Times Cited: 23
Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care
Ke‐Wei Chen, Yu-Chen Wang, Meng-Hsuan Liu, et al.
Frontiers in Cardiovascular Medicine (2022) Vol. 9
Open Access | Times Cited: 26
A Review of the Relationship between the Immune Response, Inflammation, Oxidative Stress, and the Pathogenesis of Sickle Cell Anaemia
Florence Ifechukwude Aboderin, Taofeeq Oduola, Glenda M. Davison, et al.
Biomedicines (2023) Vol. 11, Iss. 9, pp. 2413-2413
Open Access | Times Cited: 12
Machine learning based assessment of modifiable and non-modifiable risk factors associated with acute conary syndrome
Shital Hajare, Rajendra Rewatkar, K. T. V. Reddy
AIP conference proceedings (2025) Vol. 3233, pp. 020038-020038
Closed Access
Preclinical identification of acute coronary syndrome without high sensitivity troponin assays using machine learning algorithms
Andreas Goldschmied, Manuel Sigle, Wenke Faller, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
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
A Review of the Relationship between the Immune Response, Inflammation, Oxidative Stress, and the Pathogenesis of Sickle cell Anaemia
Florence Ifechukwude Aboderin, Taofeeq Oduola, Glenda M. Davison, et al.
(2023)
Open Access | Times Cited: 5
Prediction of emergency department presentations for acute coronary syndrome using a machine learning approach
Vincent C Kurucz, Jimmy Schenk, Denise P. Veelo, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 1
Selective classification with machine learning uncertainty estimates improves ACS prediction: A retrospective study in the prehospital setting.
Juan José López García, Rebecca Rutherford Kitzmiller, Ashok Krishnamurthy, et al.
Research Square (Research Square) (2024)
Open Access | Times Cited: 1
A bibliometric analysis of acute myocardial infarction in women from 2000 to 2022
Ming Biao Xu, Fupeng Yang, Bin Shen, et al.
Frontiers in Cardiovascular Medicine (2023) Vol. 10
Open Access | Times Cited: 3
Identifying and Solving Gaps in Pre- and In-Hospital Acute Myocardial Infarction Care in Asia-Pacific Countries
Paul Jie Wen Tern, Amar Vaswani, Khung Keong Yeo
Korean Circulation Journal (2023) Vol. 53, Iss. 9, pp. 594-594
Open Access | Times Cited: 3
Classification of Prehospital Electrocardiograms Performed in Ambulances According to Severity Using a Deep Learning Neural Network
Ryo Oikawa, Akio Doi, Tomonori Itoh, et al.
Emergency Care and Medicine (2024) Vol. 1, Iss. 3, pp. 280-298
Open Access
Machine learning based prediction model for acute coronary syndrome using biomarker
Shital Hajare, Rajendra Rewatkar, K. T. V. Reddy
AIP conference proceedings (2024) Vol. 3188, pp. 080024-080024
Closed Access
INTERNATIONAL EVALUATION OF AN ARTIFICIAL INTELLIGENCE-POWERED ECG MODEL DETECTING OCCLUSION MYOCARDIAL INFARCTION
Robert Herman, H. Pendell Meyers, Stephen W. Smith, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access
DEVELOPMENT OF A MACHINE-LEARNING ALGORITHM FOR PREDICTING THE ARRIVAL TIME AND ASSESSING FACTORS INFLUENCING PATIENT ARRIVAL AT HOSPITAL EMERGENCY DEPARTMENT – PRELIMINARY STUDY
Ilona Karpiel, Olga Zięba
The Polish Journal of Aviation Medicine Bioengineering and Psychology (2023) Vol. 26, Iss. 4, pp. 26-36
Open Access
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