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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:

Prediction of ICU admission for COVID-19 patients: a Machine Learning approach based on Complete Blood Count data
Lorenzo Famiglini, Giorgio Bini, Anna Carobene, et al.
(2021), pp. 160-165
Open Access | Times Cited: 18

Showing 18 citing articles:

Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients
Sara Saadatmand, Khodakaram Salimifard, Reza Mohammadi, et al.
Annals of Operations Research (2022) Vol. 328, Iss. 1, pp. 1043-1071
Open Access | Times Cited: 32
How is test laboratory data used and characterised by machine learning models? A systematic review of diagnostic and prognostic models developed for COVID-19 patients using only laboratory data
Anna Carobene, Frida Milella, Lorenzo Famiglini, et al.
Clinical Chemistry and Laboratory Medicine (CCLM) (2022) Vol. 60, Iss. 12, pp. 1887-1901
Open Access | Times Cited: 28
Predictive models for COVID-19 detection using routine blood tests and machine learning
Yury V. Kistenev, Denis A. Vrazhnov, Ekaterina E. Shnaider, et al.
Heliyon (2022) Vol. 8, Iss. 10, pp. e11185-e11185
Open Access | Times Cited: 26
A robust and parsimonious machine learning method to predict ICU admission of COVID-19 patients
Lorenzo Famiglini, Andrea Campagner, Anna Carobene, et al.
Medical & Biological Engineering & Computing (2022)
Open Access | Times Cited: 22
Complete blood count as a biomarker for preeclampsia with severe features diagnosis: a machine learning approach
Daniella Castro Araújo, Alexandre Afonso de Macedo, Adriano Veloso, et al.
BMC Pregnancy and Childbirth (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 3
A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19
Rita Murri, Jacopo Lenkowicz, C. Masciocchi, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 18
Machine Learning-Driven Prediction of ICU Admissions for COVID-19 Patients
A. M. Mutawa
(2024)
Open Access
Predictive Modeling of COVID-19 Patient Recovery Using Complete Blood Count Data
Manav Shah, Omshree Sorathia, Dev Talanpuri, et al.
Algorithms for intelligent systems (2024), pp. 49-61
Closed Access
Use of machine learning for triage and transfer of ICU patients in the Covid-19 pandemic period: Scope Review
Lia Da Graça, Lucio Padrini, Richarlisson Moraes, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 1
Prognostic Stacking Machine Learning Model for Intensive Care Unit Admission Prediction of COVID Patients
Richa Sharma, Himanshu Pandey, Ambuj Kumar Agarwal, et al.
Lecture notes in networks and systems (2023), pp. 509-518
Closed Access | Times Cited: 1
Deep Learning Based Multi-Label Prediction of Hospitalization for COVID-19 Cases
Carson K. Leung, Thanh Huy Daniel, Nguyen Tran
(2022) Vol. 28, pp. 96-101
Closed Access | Times Cited: 2
Machine Learning and Laboratory Values in the Diagnosis, Prognosis and Vaccination Strategy of COVID-19
Anna Carobene, Lorenzo Famiglini, Eleonora Sabetta, et al.
Springer eBooks (2022), pp. 121-156
Closed Access | Times Cited: 1
Analysis and Application of Regression Models to ICU Patient Monitoring
Sergio Celada-Bernal, Carlos M. Travieso, Guillermo Pérez-Acosta, et al.
Studies in computational intelligence (2023), pp. 301-318
Closed Access
Predict Admission of Confirmed COVID-19 Cases to ICU
K. Shanmukh Akul, P.Y.R. Pavani, A. Pradnesh, et al.
International Journal of Computer Engineering in Research Trends (2023) Vol. 10, Iss. 4, pp. 199-203
Open Access
HGSOXGB: Hunger-Games-Search-Optimization-Based Framework to Predict the Need for ICU Admission for COVID-19 Patients Using eXtreme Gradient Boosting
Farhana Tazmim Pinki, Md. Abdul Awal, Khondoker Mirazul Mumenin, et al.
Mathematics (2023) Vol. 11, Iss. 18, pp. 3960-3960
Open Access
Reconstructing the cytokine view for the multi-view prediction of COVID-19 mortality
Yueying Wang, Zhao Wang, Yaqing Liu, et al.
BMC Infectious Diseases (2023) Vol. 23, Iss. 1
Open Access
Developing an interpretable machine learning model for predicting COVID-19 patients deteriorating prior to intensive care unit admission using laboratory markers
Alejandro Reina Reina, Jose Manuel Barrera, Alejandro Maté, et al.
Heliyon (2023) Vol. 9, Iss. 12, pp. e22878-e22878
Open Access
Bleak Medical Prognosis of Covid Patients Using Explainable Machine Learning
Richa Sharma, Himanshu Pandey, Ambuj Kumar Agarwal, et al.
(2023), pp. 1-5
Closed Access
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