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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Biomedical document relation extraction with prompt learning and KNN.

Di Zhao¹, Yumeng Yang², Peng Chen²

¹School of Computer Science and Engineering, Dalian Minzu University, 116650 Dalian, China.

Journal of Biomedical Informatics

|August 2, 2023

Summary

This summary is machine-generated.

This study introduces a simplified approach to biomedical relation extraction using prompt learning and the T5 model. The new method improves document semantic understanding and achieves better performance on benchmark datasets.

Keywords:

Document relation extraction KNN Pretrained language model Prompt learning

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Area of Science:

Biomedical informatics
Natural Language Processing
Machine Learning

Background:

Document-level relation extraction identifies relationships between entities across sentences.
Current methods often rely on complex graph-based or pre-trained language models.
These existing approaches can lead to intricate and computationally intensive workflows.

Purpose of the Study:

To propose a simplified and effective method for biomedical relation extraction.
To leverage prompt learning with the T5 model for document relation extraction.
To introduce a few-shot relation extraction technique using K-nearest neighbors (KNN) and prompt learning.

Main Methods:

A novel model combining prompt learning with the T5 architecture, incorporating a mask template mechanism.
A few-shot relation extraction strategy utilizing the K-nearest neighbor (KNN) algorithm to identify similar semantic labels.
Evaluation on two established biomedical document benchmarks.

Main Results:

The proposed model enhances the learning of document semantic information.
Demonstrated improvement in relation extraction performance, with a 3.1% increase in F1 score on the CDR dataset.
The prompt learning approach simplifies the relation extraction process while maintaining effectiveness.

Conclusions:

Prompt learning offers a viable and efficient alternative for document-level biomedical relation extraction.
The integration of T5 and prompt learning, along with KNN for few-shot learning, provides a robust framework.
The findings suggest potential for improved accuracy and reduced complexity in biomedical NLP tasks.