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Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

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Detecting hedge cues and their scope in biomedical text with conditional random fields.

Shashank Agarwal¹, Hong Yu

¹Medical Informatics, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.

Journal of Biomedical Informatics

|August 17, 2010

Summary

This study developed a robust algorithm using conditional random fields (CRFs) to accurately detect hedge cues and their scope in biomedical literature and clinical notes, improving text-mining applications.

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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Area of Science:

Biomedical Natural Language Processing
Computational Linguistics
Machine Learning in Healthcare

Background:

Hedging language is prevalent in biomedical literature and clinical notes, expressing uncertainty.
Accurate detection of hedge cues and their scope is crucial for text-mining applications to avoid misinterpreting uncertain statements as facts.
Identifying hedging in text is challenging due to linguistic complexity.

Purpose of the Study:

To develop an automated algorithm for detecting hedge cues and their scope within biomedical literature.
To enhance the accuracy of text-mining tools by correctly identifying uncertain information.

Main Methods:

Utilized conditional random fields (CRFs), a supervised machine-learning algorithm.
Trained CRF models on the publicly available BioScope corpus.
Evaluated model performance using recall, precision, and F1-score, comparing against baseline systems.

Main Results:

The best CRF-based model achieved high F1-scores: 88% and 86% for biological literature, and 93% and 90% for clinical notes.
The developed models demonstrated statistically significant improvements over baseline systems.
The system effectively identifies hedge cue phrases and their corresponding scopes.

Conclusions:

The developed algorithm is robust, capable of identifying hedge cues and their scope in both biological and clinical text.
The system is publicly available as a Java API and an online tool to aid text-mining applications.
This represents the first publicly accessible system for detecting hedge cues and their scope in biomedical literature.