Artificial Intelligence based Healthcare Knowledge Structure for Automatic Medical Report Generation DOI
Pramod Kumar, Faisal Yousef Alghayadh,

Vinay Prasad Tamta

et al.

Published: Sept. 18, 2024

Language: Английский

Self-supervised learning for graph-structured data in healthcare applications: A comprehensive review DOI
Safa Ben Atitallah, Chaima Ben Rabah, Maha Driss

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 188, P. 109874 - 109874

Published: Feb. 24, 2025

Language: Английский

Citations

1
A contemporary survey of recent advances in federated learning: Taxonomies, applications, and challenges DOI
Mohammed H. Alsharif, Raju Kannadasan, Wei Wei

et al.

Internet of Things, Journal Year: 2024, Volume and Issue: 27, P. 101251 - 101251

Published: June 15, 2024

Language: Английский

Citations

4
Data-driven prediction of spinal cord injury recovery: An exploration of current status and future perspectives DOI Creative Commons
Samuel Håkansson, Miklovana Tuci, Marc Bolliger

et al.

Experimental Neurology, Journal Year: 2024, Volume and Issue: 380, P. 114913 - 114913

Published: Aug. 2, 2024

Spinal Cord Injury (SCI) presents a significant challenge in rehabilitation medicine, with recovery outcomes varying widely among individuals. Machine learning (ML) is promising approach to enhance the prediction of trajectories, but its integration into clinical practice requires thorough understanding efficacy and applicability. We systematically reviewed current literature on data-driven models SCI prediction. The included studies were evaluated based range criteria assessing approach, implementation, input data preferences, aimed forecast. observe tendency utilize routinely acquired data, such as International Standards for Neurological Classification (ISNCSCI), imaging, demographics, functional derived from Independence Measure (SCIM) III Functional (FIM) scores focus motor ability. Although there has been an increasing interest over time, traditional machine architectures, linear regression tree-based approaches, remained overwhelmingly popular choices implementation. This implies ample opportunities exploring architectures addressing challenges predicting recovery, including techniques limited longitudinal improving generalizability, enhancing reproducibility. conclude perspective, highlighting possible future directions drawing parallels other application fields terms diverse types (imaging, tabular, sequential, multimodal), (limited, missing, data), algorithmic needs (causal inference, robustness).

Language: Английский

Citations

4
SASD: Self-Attention for Small Datasets—A case study in smart villages DOI
Daniel Bolaños-Martinez,

Alberto Durán-López,

José Luis García Garrido

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126245 - 126245

Published: Jan. 1, 2025

Language: Английский

Citations

0
Inductive graph neural network framework for imputation of single-cell RNA sequencing data DOI

V K Boneshwar,

Deepesh Agarwal, Balasubramaniam Natarajan

et al.

Computers & Chemical Engineering, Journal Year: 2025, Volume and Issue: 195, P. 109031 - 109031

Published: Feb. 6, 2025

Language: Английский

Citations

0
Hospital Network Resilience in Hatay Following the 2023 Cascading Earthquakes in Türkiye: Year-Long Investigation DOI
Milad Roohi, Derya Deniz,

Milad Cheraghzade

et al.

Journal of Structural Engineering, Journal Year: 2025, Volume and Issue: 151(5)

Published: Feb. 26, 2025

Language: Английский

Citations

0
A systematic mapping study on graph machine learning for static source code analysis DOI Creative Commons
Jesse Maarleveld, Jiapan Guo, Daniel Feitosa

et al.

Information and Software Technology, Journal Year: 2025, Volume and Issue: unknown, P. 107722 - 107722

Published: March 1, 2025

Language: Английский

Citations

0
Brain-GCN-Net: Graph-Convolutional Neural Network for brain tumor identification DOI
Ercan Gürsoy, Yasin Kaya

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 180, P. 108971 - 108971

Published: Aug. 5, 2024

Language: Английский

Citations

3
Data-driven prediction of spinal cord injury recovery: an exploration of current status and future perspectives DOI Creative Commons
Samuel Håkansson, Miklovana Tuci, Marc Bolliger

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 4, 2024

Abstract Spinal Cord Injury (SCI) presents a significant challenge in rehabilitation medicine, with recovery outcomes varying widely among individuals. Machine learning (ML) is promising approach to enhance the prediction of trajectories, but its integration into clinical practice requires thorough understanding efficacy and applicability. We systematically reviewed current literature on data-driven models SCI prediction. The included studies were evaluated based range criteria assessing approach, implementation, input data preferences, aimed forecast. observe tendency utilize routinely acquired data, such as International Standards for Neurological Classification (ISNCSCI), imaging, demographics, functional derived from Independence Measure (SCIM) III Functional (FIM) scores focus motor ability. Although there has been an increasing interest over time, traditional machine architectures, linear regression tree-based approaches, remained overwhelmingly popular choices implementation. This implies ample opportunities exploring architectures addressing challenges predicting recovery, including techniques limited longitudinal improving generalizability, enhancing reproducibility. conclude perspective, highlighting possible future directions drawing parallels other application fields terms diverse types (imaging, tabular, sequential, multimodal), (limited, missing, data), algorithmic needs (causal inference, robustness).

Language: Английский

Citations

1
Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review DOI Creative Commons
Esmaeel Toni, Haleh Ayatollahi,

Reza Abbaszadeh

et al.

Pharmaceuticals, Journal Year: 2024, Volume and Issue: 17(6), P. 795 - 795

Published: June 17, 2024

Background: Drug safety relies on advanced methods for timely and accurate prediction of side effects. To tackle this requirement, scoping review examines machine-learning approaches predicting drug-related effects with a particular focus chemical, biological, phenotypical features. Methods: This was in which comprehensive search conducted various databases from 1 January 2013 to 31 December 2023. Results: The results showed the widespread use Random Forest, k-nearest neighbor, support vector machine algorithms. Ensemble methods, particularly random forest, emphasized significance integrating chemical biological features Conclusions: article considering variety features, datasets, learning algorithms Forest best performance combining improved prediction. suggested that techniques have some potential improve drug development trials. Future work should specific feature types, selection techniques, graph-based even better

Language: Английский

Citations

1