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An adaptive annotation approach for biomedical entity and relation recognition.

Seid Muhie Yimam¹, Chris Biemann², Ljiljana Majnaric³

¹TU Darmstadt CS Department, FG Language Technology, 64289, Darmstadt, Germany. seidymam@gmail.com.

Brain Informatics

|October 18, 2016

Summary

This summary is machine-generated.

Interactive machine learning accelerates the creation of high-quality biomedical datasets. A human-in-the-loop approach iteratively improves models, significantly speeding up annotation for medical entity recognition and relation extraction.

Keywords:

Biomedical entity recognition Data mining Human in the loop Interactive annotation Knowledge discovery Machine learning Relation learning

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Area of Science:

Biomedical Informatics
Machine Learning
Natural Language Processing

Background:

Classical machine learning relies on static training and test sets.
Developing large, high-quality annotated datasets is time-consuming and resource-intensive.
Human-in-the-loop approaches offer a dynamic alternative for iterative model improvement.

Purpose of the Study:

To demonstrate the impact and efficiency of interactive machine learning for biomedical dataset development.
To introduce a human-in-the-loop approach for iterative annotation of biomedical entities and relations.
To validate the proposed method through multiple experimental simulations and case studies.

Main Methods:

Developing a biomedical entity recognition dataset using a human-in-the-loop framework.
Implementing an iterative annotation process where the model proposes labels based on prior annotations.
Conducting three experiments: simulation of iterative annotation, a clinical doctor case study, and semantic relation annotation.

Main Results:

A handful of annotated medical abstracts significantly increased annotation speed in simulations.
Clinical doctors successfully annotated relevant medical terms, validating practical application.
The method proved effective for annotating semantic relations across documents, confirmed quantitatively and qualitatively.

Conclusions:

Interactive machine learning, particularly the human-in-the-loop approach, substantially accelerates the development of quality biomedical datasets.
This iterative annotation strategy enhances efficiency for tasks like entity recognition and relation extraction.
The validated method enables more personalized and responsive information extraction technologies in the biomedical domain.