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Related Experiment Video

Updated: Mar 13, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks.

Qikang Wei¹, Tao Chen¹, Ruifeng Xu²

¹Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China and.

Database : the Journal of Biological Databases and Curation

|October 26, 2016

Summary

This study introduces a novel system for recognizing and normalizing disease names in biomedical texts. The system achieves high accuracy, addressing a gap in available tools for disease named entity recognition.

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: August 3, 2018

Area of Science:

Biomedical Informatics
Natural Language Processing
Information Extraction

Background:

Recognizing disease and chemical named entities is crucial for biomedical information extraction.
Disease name recognition is challenging due to name diversity and complexity.
Publicly available disease named entity recognition systems are scarce compared to chemical systems.

Purpose of the Study:

To develop and evaluate a system for disease named entity recognition (DNER) and normalization.
To combine results from two distinct DNER models for improved performance.
To normalize recognized disease entities to standardized medical terms.

Main Methods:

Developed two DNER models: one using conditional random fields with rule-based post-processing, and another using bidirectional recurrent neural networks.
Integrated DNER model outputs using a support vector machine classifier.
Employed a vector space model for normalizing recognized disease entities to Medical Subject Headings (MeSH).

Main Results:

The proposed system achieved an F1-measure of 0.8428 at the mention level.
The system achieved an F1-measure of 0.7804 at the concept level.
Performance was evaluated on the BioCreative V chemical-disease relation task dataset.

Conclusions:

The developed system effectively performs disease named entity recognition and normalization.
Combining multiple DNER models enhances recognition accuracy.
The system provides a valuable tool for biomedical text mining and information retrieval.