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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.

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Showing 1-25 of 26 citing articles:

Deep learning for brain tumor classification
Justin S. Paul, Andrew J. Plassard, Bennett A. Landman, et al.
Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2017) Vol. 10137, pp. 1013710-1013710
Open Access | Times Cited: 235
A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions
Shriniket Dixit, Anant Kumar, Kathiravan Srinivasan
Diagnostics (2023) Vol. 13, Iss. 7, pp. 1353-1353
Open Access | Times Cited: 61
Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners
Luis Bote-Curiel, Sergio Muñoz-Romero, Alicia Gerrero-Curieses, et al.
Applied Sciences (2019) Vol. 9, Iss. 11, pp. 2331-2331
Open Access | Times Cited: 97
Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press
Balduíno César Mateus, Mateus Mendes, José Torres Farinha, et al.
Energies (2021) Vol. 14, Iss. 21, pp. 6958-6958
Open Access | Times Cited: 95
Application of machine learning techniques to the analysis and prediction of drug pharmacokinetics
Ryosaku Ota, Fumiyoshi Yamashita
Journal of Controlled Release (2022) Vol. 352, pp. 961-969
Closed Access | Times Cited: 37
Multimodal and multicontrast image fusion via deep generative models
Giovanna Maria Dimitri, S Spasov, Andrea Duggento, et al.
Information Fusion (2022) Vol. 88, pp. 146-160
Open Access | Times Cited: 29
A Review of Automatic Phenotyping Approaches using Electronic Health Records
Hadeel Alzoubi, Raid Alzubi, Naeem Ramzan, et al.
Electronics (2019) Vol. 8, Iss. 11, pp. 1235-1235
Open Access | Times Cited: 50
A multi-task Gaussian process self-attention neural network for real-time prediction of the need for mechanical ventilators in COVID-19 patients
Kai Zhang, Siddharth Karanth, Bela Patel, et al.
Journal of Biomedical Informatics (2022) Vol. 130, pp. 104079-104079
Open Access | Times Cited: 18
BIR: Biomedical Information Retrieval System for Cancer Treatment in Electronic Health Record Using Transformers
Pir Noman Ahmad, Yuanchao Liu, Khalid S. Khan, et al.
Sensors (2023) Vol. 23, Iss. 23, pp. 9355-9355
Open Access | Times Cited: 10
Unsupervised discovery of clinical disease signatures using probabilistic independence
Thomas A. Lasko, William W. Stead, John M. Still, et al.
Journal of Biomedical Informatics (2025), pp. 104837-104837
Open Access
Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithms
David J. Albers, Noémie Elhadad, Jan Claassen, et al.
Journal of Biomedical Informatics (2018) Vol. 78, pp. 87-101
Open Access | Times Cited: 22
Measuring the effect of different types of unsupervised word representations on Medical Named Entity Recognition
Arantza Casillas, Nerea Ezeiza, Iakes Goenaga, et al.
International Journal of Medical Informatics (2019) Vol. 129, pp. 100-106
Closed Access | Times Cited: 15
A health informatics transformation model based on intelligent cloud computing – exemplified by type 2 diabetes mellitus with related cardiovascular diseases
Hsueh-Chun Lin, Yu‐Chen Kuo, Mengyu Liu
Computer Methods and Programs in Biomedicine (2020) Vol. 191, pp. 105409-105409
Closed Access | Times Cited: 13
Use of learning approaches to predict clinical deterioration in patients based on various variables: a review of the literature
Tariq Ibrahim Al-Shwaheen, Mehrdad Moghbel, Yuan Wen Hau, et al.
Artificial Intelligence Review (2021) Vol. 55, Iss. 2, pp. 1055-1084
Closed Access | Times Cited: 11
Temporal-Spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit
Weizhi Nie, Yuhe Yu, Chen Zhang, et al.
IEEE Transactions on Biomedical Engineering (2023) Vol. 71, Iss. 2, pp. 583-595
Open Access | Times Cited: 3
Phenotyping hypotensive patients in critical care using hospital discharge summaries
Yang Dai, Sharukh Lokhandwala, William J. Long, et al.
(2017), pp. 401-404
Open Access | Times Cited: 7
Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Models
Sibghat Ullah, Xu Zhao, Hao Wang, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2020), pp. 1-9
Open Access | Times Cited: 7
VERONICA: Visual Analytics for Identifying Feature Groups in Disease Classification
Neda Rostamzadeh, Sheikh S. Abdullah, Kamran Sedig, et al.
Information (2021) Vol. 12, Iss. 9, pp. 344-344
Open Access | Times Cited: 7
Classifying Leukemia and Gout Patients with Neural Networks
Guryash Bahra, Lena Wiese
Communications in computer and information science (2018), pp. 150-160
Closed Access | Times Cited: 4
Prediction Model for Identifying Computational Phenotypes of Children with Cerebral Palsy Needing Neurotoxin Treatments
Carlo M. Bertoncelli, M Latalski, Domenico Bertoncelli, et al.
Toxins (2022) Vol. 15, Iss. 1, pp. 20-20
Open Access | Times Cited: 3
Time Series Feature Learning with Applications to Health Care
Zhengping Che, Sanjay Purushotham, David C. Kale, et al.
Springer eBooks (2017), pp. 389-409
Closed Access | Times Cited: 3
Completion of irregular emotion sequence based on users' social relationships and historical emotions
Qiang Liu, Hao Li, Chunzhi Xie, et al.
International Journal of Parallel Emergent and Distributed Systems (2024), pp. 1-19
Closed Access
Learning Medical Subject Headings in PubMed Articles to Enhance Deep Predictions
Zolzaya Dashdorj, Zoljargal Jargalsaikhan, Stanislav Grigorev, et al.
(2024), pp. 000371-000374
Closed Access
Parallel and Distributed Processing for Unsupervised Patient Phenotype Representation
John Anderson Garcia Henao, Fŕed́eric Precioso, Pascal Staccini, et al.
Communications in computer and information science (2019), pp. 3-17
Closed Access | Times Cited: 2
Phenome-Wide Association Study
Jeremy L. Warner, Joshua C. Denny
Elsevier eBooks (2015), pp. 83-113
Closed Access | Times Cited: 1
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