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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:

Big Data and Data Science in Critical Care
L. Nelson Sanchez‐Pinto, Yuan Luo, Matthew M. Churpek
CHEST Journal (2018) Vol. 154, Iss. 5, pp. 1239-1248
Open Access | Times Cited: 248

Showing 1-25 of 248 citing articles:

Gut microbiome, big data and machine learning to promote precision medicine for cancer
Giovanni Cammarota, Gianluca Ianiro, Anna M. Ahern, et al.
Nature Reviews Gastroenterology & Hepatology (2020) Vol. 17, Iss. 10, pp. 635-648
Closed Access | Times Cited: 242
Multimodal machine learning in precision health: A scoping review
Adrienne Kline, Hanyin Wang, Yikuan Li, et al.
npj Digital Medicine (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 220
Artificial intelligence-based clinical decision support in pediatrics
Sriram Ramgopal, L. Nelson Sanchez‐Pinto, Christopher M. Horvat, et al.
Pediatric Research (2022) Vol. 93, Iss. 2, pp. 334-341
Open Access | Times Cited: 77
Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation
Damien Gruson, Thibault Helleputte, Patrick Rousseau, et al.
Clinical Biochemistry (2019) Vol. 69, pp. 1-7
Closed Access | Times Cited: 116
Patient-ventilator asynchronies during mechanical ventilation: current knowledge and research priorities
Candelaria de Haro, A. Ochagavía, Josefina López‐Aguilar, et al.
Intensive Care Medicine Experimental (2019) Vol. 7, Iss. S1
Open Access | Times Cited: 106
Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey
Oliver Maaßen, Sebastian Fritsch, Julia Palm, et al.
Journal of Medical Internet Research (2021) Vol. 23, Iss. 3, pp. e26646-e26646
Open Access | Times Cited: 83
Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19
Gunjan Arora, Jayadev Joshi, Rahul Shubhra Mandal, et al.
Pathogens (2021) Vol. 10, Iss. 8, pp. 1048-1048
Open Access | Times Cited: 74
The use of machine learning and artificial intelligence within pediatric critical care
Neel Shah, Ahmed Arshad, Monty B. Mazer, et al.
Pediatric Research (2022) Vol. 93, Iss. 2, pp. 405-412
Open Access | Times Cited: 39
State of the Art of Machine Learning–Enabled Clinical Decision Support in Intensive Care Units: Literature Review
Na Hong, Chun Liu, Jianwei Gao, et al.
JMIR Medical Informatics (2022) Vol. 10, Iss. 3, pp. e28781-e28781
Open Access | Times Cited: 38
Minimizing bias when using artificial intelligence in critical care medicine
Benjamin L. Ranard, Soojin Park, Yugang Jia, et al.
Journal of Critical Care (2024) Vol. 82, pp. 154796-154796
Closed Access | Times Cited: 10
Application of machine learning in rheumatic disease research
Ki‐Jo Kim, Ilias Tagkopoulos
The Korean Journal of Internal Medicine (2019) Vol. 34, Iss. 4, pp. 708-722
Open Access | Times Cited: 72
Intensive Care Unit Telemedicine in the Era of Big Data, Artificial Intelligence, and Computer Clinical Decision Support Systems
Ryan Kindle, Omar Badawi, Leo Anthony Celi, et al.
Critical Care Clinics (2019) Vol. 35, Iss. 3, pp. 483-495
Closed Access | Times Cited: 56
The evolution of data science and big data research: A bibliometric analysis
Daphne R. Raban, Avishag Gordon
Scientometrics (2020) Vol. 122, Iss. 3, pp. 1563-1581
Open Access | Times Cited: 54
Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review
Mahanazuddin Syed, Shorabuddin Syed, Kevin W. Sexton, et al.
Informatics (2021) Vol. 8, Iss. 1, pp. 16-16
Open Access | Times Cited: 47
Exploring non-traditional learner motivations and characteristics in online learning: A learner profile study
Andrew Zamecnik, Vitomir Kovanović, Srécko Joksimovíc, et al.
Computers and Education Artificial Intelligence (2022) Vol. 3, pp. 100051-100051
Open Access | Times Cited: 31
Logistic regression technique is comparable to complex machine learning algorithms in predicting cognitive impairment related to post intensive care syndrome
Ting Wu, YueQing Wei, Jing-Bing Wu, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 21
Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research
Kai Sun, Arkajyoti Roy, Joshua M. Tobin
Journal of Critical Care (2024) Vol. 82, pp. 154792-154792
Closed Access | Times Cited: 7
Digital solutions in paediatric sepsis: current state, challenges, and opportunities to improve care around the world
L. Nelson Sanchez‐Pinto, María del Pilar Arias López, Halden F. Scott, et al.
The Lancet Digital Health (2024) Vol. 6, Iss. 9, pp. e651-e661
Open Access | Times Cited: 7
Assessing SOFA score trajectories in sepsis using machine learning: A pragmatic approach to improve the accuracy of mortality prediction
Lars Palmowski, Hartmuth Nowak, Andrea Witowski, et al.
PLoS ONE (2024) Vol. 19, Iss. 3, pp. e0300739-e0300739
Open Access | Times Cited: 6
Prediction of carbapenem-resistant gram-negative bacterial bloodstream infection in intensive care unit based on machine learning
Qiqiang Liang, Shuo Ding, Juan Chen, et al.
BMC Medical Informatics and Decision Making (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 6
From bytes to bedside: a systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit
J. Schouten, Melissa A. C. M. Kalden, Eris van Twist, et al.
Intensive Care Medicine (2024)
Open Access | Times Cited: 6
Autonomic Nervous System Dysfunction in Pediatric Sepsis
Colleen M. Badke, Lauren Marsillio, Debra E. Weese‐Mayer, et al.
Frontiers in Pediatrics (2018) Vol. 6
Open Access | Times Cited: 48
Reporting of demographic data and representativeness in machine learning models using electronic health records
Selen Bozkurt, Eli M. Cahan, Martin Seneviratne, et al.
Journal of the American Medical Informatics Association (2020) Vol. 27, Iss. 12, pp. 1878-1884
Open Access | Times Cited: 46
Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials
Pratik Sinha, Alexandra B. Spicer, Kevin Delucchi, et al.
EBioMedicine (2021) Vol. 74, pp. 103697-103697
Open Access | Times Cited: 33
Development and Validation of Machine Learning Models for Real-Time Mortality Prediction in Critically Ill Patients With Sepsis-Associated Acute Kidney Injury
Xiao-Qin Luo, Ping Yan, Shao-Bin Duan, et al.
Frontiers in Medicine (2022) Vol. 9
Open Access | Times Cited: 24
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