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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:

A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication
Negar Farzaneh, Craig A. Williamson, Jonathan Gryak, et al.
npj Digital Medicine (2021) Vol. 4, Iss. 1
Open Access | Times Cited: 43

Showing 1-25 of 43 citing articles:

Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022)
Hui Wen Loh, Chui Ping Ooi, Silvia Seoni, et al.
Computer Methods and Programs in Biomedicine (2022) Vol. 226, pp. 107161-107161
Open Access | Times Cited: 413
Restoring and attributing ancient texts using deep neural networks
Yannis Assael, Thea Sommerschield, Brendan Shillingford, et al.
Nature (2022) Vol. 603, Iss. 7900, pp. 280-283
Open Access | Times Cited: 110
Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models
Joseph D. Janizek, Ayse B. Dincer, Safiye Çelik, et al.
Nature Biomedical Engineering (2023) Vol. 7, Iss. 6, pp. 811-829
Closed Access | Times Cited: 28
A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter
Sowmya Mangalath Ravindran, Santosh Kumar Moorakkal Bhaskaran, Sooraj K. Ambat
Environmental Processes (2021) Vol. 8, Iss. 4, pp. 1567-1599
Open Access | Times Cited: 41
Predicting outcome of patients with prolonged disorders of consciousness using machine learning models based on medical complexity
Piergiuseppe Liuzzi, Alfonso Magliacano, Francesco De Bellis, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 27
Predicting posttraumatic epilepsy using admission electroencephalography after severe traumatic brain injury
Matthew Pease, Jonathan Elmer, Ameneh Zare Shahabadi, et al.
Epilepsia (2023) Vol. 64, Iss. 7, pp. 1842-1852
Closed Access | Times Cited: 13
Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future
Kyung Ah Kim, Hakseung Kim, Eun Jin Ha, et al.
Journal of Korean Neurosurgical Society (2024) Vol. 67, Iss. 5, pp. 493-509
Open Access | Times Cited: 5
Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications
Sana Hashemi, Zohreh Yousefzadeh, Ahmad Ali Abin, et al.
Deleted Journal (2024) Vol. 9, Iss. 1
Open Access | Times Cited: 5
Permutation-Based Hypothesis Testing for Neural Networks
Francesca Mandel, Ian Barnett
Proceedings of the AAAI Conference on Artificial Intelligence (2024) Vol. 38, Iss. 13, pp. 14306-14314
Open Access | Times Cited: 5
Early identification of autism spectrum disorder based on machine learning with eye-tracking data
Qiuhong Wei, Wenxin Dong, Dongchuan Yu, et al.
Journal of Affective Disorders (2024) Vol. 358, pp. 326-334
Closed Access | Times Cited: 5
Predicting clinical pathways of traumatic brain injuries (TBIs) through process mining
Mansoureh Yari Eili, Jalal Rezaeenour, Mohammad Hossein Roozbahani
npj Digital Medicine (2025) Vol. 8, Iss. 1
Open Access
Machine Learning Approaches to Prognostication in Traumatic Brain Injury
Neeraj Badjatia, Jamie Podell, Ryan Felix, et al.
Current Neurology and Neuroscience Reports (2025) Vol. 25, Iss. 1
Closed Access
Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation—A Review
Seun Orenuga, Philip Jordache, Daniel Mirzai, et al.
Life (2025) Vol. 15, Iss. 3, pp. 424-424
Open Access
AI-Based Decision Support System for Traumatic Brain Injury: A Survey
Flora Rajaei, Shuyang Cheng, Craig A. Williamson, et al.
Diagnostics (2023) Vol. 13, Iss. 9, pp. 1640-1640
Open Access | Times Cited: 11
A self-supervised learning-based approach to clustering multivariate time-series data with missing values (SLAC-Time): An application to TBI phenotyping
Hamid Ghaderi, Brandon Foreman, Amin Nayebi, et al.
Journal of Biomedical Informatics (2023) Vol. 143, pp. 104401-104401
Open Access | Times Cited: 11
Demystifying the Black Box: The Importance of Interpretability of Predictive Models in Neurocritical Care
Laura Moss, David Corsar, Martin Shaw, et al.
Neurocritical Care (2022) Vol. 37, Iss. S2, pp. 185-191
Open Access | Times Cited: 18
The leap to ordinal: Detailed functional prognosis after traumatic brain injury with a flexible modelling approach
Shubhayu Bhattacharyay, Ioan Milosevic, Lindsay Wilson, et al.
PLoS ONE (2022) Vol. 17, Iss. 7, pp. e0270973-e0270973
Open Access | Times Cited: 18
Machine Learning in Neuroimaging of Traumatic Brain Injury: Current Landscape, Research Gaps, and Future Directions
Kevin Pierre, Jordan Turetsky, Abheek Raviprasad, et al.
Trauma Care (2024) Vol. 4, Iss. 1, pp. 31-43
Open Access | Times Cited: 3
Predicting autism spectrum disorder using maternal risk factors: A multi-center machine learning study
Qiuhong Wei, Yuanjie Xiao, Ting Yang, et al.
Psychiatry Research (2024) Vol. 334, pp. 115789-115789
Closed Access | Times Cited: 3
Combined Radiomics Model for Prediction of Hematoma Progression and Clinical Outcome of Cerebral Contusions in Traumatic Brain Injury
Liqiong Zhang, Qiyuan Zhuang, Guoqing Wu, et al.
Neurocritical Care (2021) Vol. 36, Iss. 2, pp. 441-451
Closed Access | Times Cited: 15
Development of a Multimodal Machine Learning-Based Prognostication Model for Traumatic Brain Injury Using Clinical Data and Computed Tomography Scans: A CENTER-TBI and CINTER-TBI Study
Atsuhiro Hibi, Michael D. Cusimano, Alexander Bilbily, et al.
Journal of Neurotrauma (2024) Vol. 41, Iss. 11-12, pp. 1323-1336
Open Access | Times Cited: 1
Discovery of generalizable TBI phenotypes using multivariate time-series clustering
Hamid Ghaderi, Brandon Foreman, Chandan K. Reddy, et al.
Computers in Biology and Medicine (2024) Vol. 180, pp. 108997-108997
Open Access | Times Cited: 1
Predicting the need for intubation within 3 h in the neonatal intensive care unit using a multimodal deep neural network
Jueng-Eun Im, Seung Park, Yu Jin Kim, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 3
Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department
Ahammed Mekkodathil, Ayman El‐Menyar, Mashhood Naduvilekandy, et al.
Diagnostics (2023) Vol. 13, Iss. 15, pp. 2605-2605
Open Access | Times Cited: 3
An interpretable neural network for outcome prediction in traumatic brain injury
Cristian Minoccheri, Craig A. Williamson, Mark R. Hemmila, et al.
BMC Medical Informatics and Decision Making (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 5
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