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Integrating K+ Entities Into Coreference Resolution on Biomedical Texts.

IEEE/ACM transactions on computational biology and bioinformatics
Biomedical Coreference Resolution focuses on identifying the coreferences in biomedical texts, which normally consists of two parts: (i) mention detection to identify textual representation of biological entities and (ii) finding their coreference li...

Joint Extraction of Biomedical Events Based on Dynamic Path Planning Strategy and Hybrid Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
Biomedical event detection is a pivotal information extraction task in molecular biology and biomedical research, which provides inspiration for the medical search, disease prevention, and new drug development. The existing methods usually detect sim...

Graph Convolutional Network With Self-Supervised Learning for Brain Disease Classification.

IEEE/ACM transactions on computational biology and bioinformatics
Brain functional network (BFN) analysis has become a popular method for identifying neurological diseases at their early stages and revealing sensitive biomarkers related to these diseases. Due to the fact that BFN is a graph with complex structure, ...

Distantly Supervised Biomedical Relation Extraction via Negative Learning and Noisy Student Self-Training.

IEEE/ACM transactions on computational biology and bioinformatics
Biomedical relation extraction aims to identify underlying relationships among entities, such as gene associations and drug interactions, within biomedical texts. Despite advancements in relation extraction in general knowledge domains, the scarcity ...

Interpretable deep learning architecture for gastrointestinal disease detection: A Tri-stage approach with PCA and XAI.

Computers in biology and medicine
GI abnormalities significantly increase mortality rates and impose considerable strain on healthcare systems, underscoring the essential requirement for rapid detection, precise diagnosis, and efficient strategic treatment. To develop a CAD system, t...

DualStreamFoveaNet: A Dual Stream Fusion Architecture With Anatomical Awareness for Robust Fovea Localization.

IEEE journal of biomedical and health informatics
Accurate fovea localization is essential for analyzing retinal diseases to prevent irreversible vision loss. While current deep learning-based methods outperform traditional ones, they still face challenges such as the lack of local anatomical landma...

Machine-learning-based identification of patients with IgA nephropathy using a computerized medical billing database.

PloS one
The billing database of the universal healthcare system in Japan potentially includes large-cohort data of patients with immunoglobulin A nephropathy, diagnosis codes aimed at billing should not be directly used for clinical research because of the r...

Medical Federated Model With Mixture of Personalized and Shared Components.

IEEE transactions on pattern analysis and machine intelligence
Although data-driven methods usually have noticeable performance on disease diagnosis and treatment, they are suspected of leakage of privacy due to collecting data for model training. Recently, federated learning provides a secure and trustable alte...

A smart CardioSenseNet framework with advanced data processing models for precise heart disease detection.

Computers in biology and medicine
Heart diseases remain one of the leading causes of death worldwide. As a result, early and accurate diagnostics have become an urgent need for treatment and management. Most of the conventional methods adopted tend to have major drawbacks: the issues...

Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition.

Scientific reports
Early detection of colorectal carcinoma (CRC), one of the most prevalent forms of cancer worldwide, significantly enhances the prognosis of patients. This research presents a new method for improving CRC detection using a deep learning ensemble with ...