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A Generic Semi-Supervised and Active Learning Framework for Biomedical Text Classification.

Christopher A Flores, Rodrigo Verschae

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|September 10, 2022

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This study introduces a semi-supervised learning framework to improve active learning for biomedical text classification. It reduces the need for manual labeling by clinical specialists by 10% without impacting classifier performance.

Area of Science:

Biomedical Informatics
Machine Learning
Natural Language Processing

Background:

Biomedical text classification demands extensive manual labeling by clinical specialists, which is resource-intensive.
Active learning (AL) aims to minimize labeling costs by selecting informative samples but often underutilizes unlabeled data.
Semi-supervised learning (SSL) can leverage unlabeled data to enhance classifier performance and data representativeness.

Purpose of the Study:

To propose a generic semi-supervised learning framework to enhance active learning for biomedical text classification.
To reduce the number of manually labeled training examples required from clinical specialists.
To improve the overall performance and efficiency of biomedical text classifiers.

Main Methods:

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The proposed framework integrates active learning with semi-supervised learning techniques.

It combines manually annotated samples selected by active learning with pseudo-labeled data generated by a classifier.

The framework was evaluated using three biomedical datasets related to obesity and smoking habits across various classification algorithms.

Main Results:

The semi-supervised framework successfully reduced the manual labeling effort by 10% across tested datasets.
Classifier performance was maintained despite the significant reduction in manually labeled training examples.
The framework's effectiveness was consistent across different classification algorithms used in the evaluation.

Conclusions:

The proposed semi-supervised learning framework effectively reduces the burden of manual annotation in biomedical text classification.
This approach offers a practical solution for creating high-performing classifiers with reduced reliance on clinical specialists' time.
The integration of AL and SSL presents a promising strategy for efficient biomedical text data analysis.