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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:

Detection of Fall Risk in Multiple Sclerosis by Gait Analysis—An Innovative Approach Using Feature Selection Ensemble and Machine Learning Algorithms
Paula Schumann, Maria Scholz, Katrin Trentzsch, et al.
Brain Sciences (2022) Vol. 12, Iss. 11, pp. 1477-1477
Open Access | Times Cited: 11

Showing 11 citing articles:

A SYSTEMATIC LITERATURE REVIEW: RECURSIVE FEATURE ELIMINATION ALGORITHMS
Arif Mudi Priyatno, Triyanna Widiyaningtyas
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) (2024) Vol. 9, Iss. 2, pp. 196-207
Open Access | Times Cited: 14
The role of miRNAs in multiple sclerosis pathogenesis, diagnosis, and therapeutic resistance
Ahmed S. Doghish, Ola Elazazy, Hend H. Mohamed, et al.
Pathology - Research and Practice (2023) Vol. 251, pp. 154880-154880
Closed Access | Times Cited: 15
Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review
Milad Yousefi, Masih Akhbari, Zhina Mohamadi, et al.
Frontiers in Neurology (2024) Vol. 15
Open Access | Times Cited: 5
Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique
Moritz Schneider, Kevin Seeser-Reich, Armin Fiedler, et al.
Sensors (2025) Vol. 25, Iss. 5, pp. 1468-1468
Open Access
Linking Pathogenesis to Fall Risk in Multiple Sclerosis
Jaymin Patel, Marcel P Fraix, Devendra K. Agrawal
Archives of Internal Medicine Research (2025) Vol. 8, Iss. 1
Open Access
Using machine learning algorithms to detect fear of falling in people with multiple sclerosis in standardized gait analysis
Paula Schumann, Katrin Trentzsch, Heidi Stölzer-Hutsch, et al.
Multiple Sclerosis and Related Disorders (2024) Vol. 88, pp. 105721-105721
Open Access | Times Cited: 3
A machine learning approach to determine the risk factors for fall in multiple sclerosis
Su Özgür, Meryem Koçaslan Toran, İsmail Toygar, et al.
BMC Medical Informatics and Decision Making (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 2
The walking surface influences vertical ground reaction force and centre of pressure data obtained with pressure-sensing insoles
Elke Warmerdam, L. Burger, Diana F. Mergen, et al.
Frontiers in Digital Health (2024) Vol. 6
Open Access | Times Cited: 1
Combining real data and expert knowledge to build a Bayesian Network — Application to assess multiple risk factors for fall among elderly people
Gulshan Sihag, Véronique Delcroix, Emmanuelle Grislin-Le Strugeon, et al.
Expert Systems with Applications (2024) Vol. 252, pp. 124106-124106
Closed Access
AI-Assisted Assessment of Fall Risk in Multiple Sclerosis: A Systematic Literature Review
Somayeh Mehrlatifan, Razieh Yousefian Molla
Multiple Sclerosis and Related Disorders (2024) Vol. 92, pp. 105918-105918
Closed Access
Making the Discrimination in the Walking Parameters of Individuals with Multiple Sclerosis and Parkinson’s Disease with Machine Learning
Sema Gül, Emel Soylu, Murat Terzi, et al.
Turkish Journal Of Neurology (2023) Vol. 29, Iss. 4, pp. 277-281
Open Access | Times Cited: 1
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