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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Implementing Informative-Based Active Learning in Biomedical Record Linkage for the Splink Package in Python.

Marko Miletic¹, Murat Sariyar¹

¹Bern University of Appl. Sciences, Switzerland.

Studies in Health Technology and Informatics

|June 30, 2023

Summary

This summary is machine-generated.

Active learning improves biomedical record linkage by efficiently selecting valuable training data for manual labeling. This strategy helps determine patient record similarity thresholds and assess problem complexity in big data contexts.

Keywords:

Active learning entropy record linkage splink

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Published on: June 13, 2025

Area of Science:

Biomedical Informatics
Machine Learning
Data Science

Background:

Determining similarity thresholds for patient record linkage is a significant challenge.
Efficiently creating labeled training data for record linkage is often an open issue.

Purpose of the Study:

To implement an efficient active learning strategy for biomedical record linkage.
To introduce a measure for assessing the usefulness of training sets in this context.

Main Methods:

Developed an active learning strategy incorporating a measure of training set usefulness.
Analyzed label frequencies to gauge problem complexity and classifier performance.

Main Results:

Active learning is highly recommended for manual training data generation in record linkage.
Label frequencies provide insights into classification difficulty and potential overfitting issues.

Conclusions:

Active learning is essential for efficient and effective biomedical record linkage, especially in big data scenarios.
The proposed strategy aids in threshold determination and complexity assessment, mitigating under- and overfitting.