Miletic, Marko; Sariyar, Murat (2023). Implementing Informative-Based Active Learning in Biomedical Record Linkage for the Splink Package in Python Studies in Health Technology and Informatics, 305, pp. 509-512. Amsterdam: IOS Press 10.3233/SHTI230545
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In biomedical record linkage, efficient determination of a threshold to decide at which level of similarity two records should be classified as belonging to the same patient is frequently still an open issue. Here, we describe how to implement an efficient active learning strategy that puts into practice a measure of usefulness of training sets for such a task. Our results show that active learning should always be considered when training data is to be produced via manual labeling. In addition to that, active learning gives a quick indication how complex a problem is by looking into the label frequencies: If the most difficult entities are always stemming from the same class, then the classifier will probably have less problems in distinguishing the classes. In big data applications, these two properties are essential, as the problems of under- and overfitting are exacerbated in such contexts.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
School of Engineering and Computer Science > Institut für Optimierung und Datenanalyse IODA School of Engineering and Computer Science |
Name: |
Miletic, Marko; Sariyar, Murat; Mantas, John; Gallos, Parisis; Zoulias, Emmanouil; Hasman, Arie; Househ, Mowafa S.; Charalampidou, Martha and Magdalinou, Andriana |
Subjects: |
Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
ISSN: |
1879-8365 |
ISBN: |
9781643684000 |
Series: |
Studies in Health Technology and Informatics |
Publisher: |
IOS Press |
Language: |
English |
Submitter: |
Murat Sariyar |
Date Deposited: |
10 Jan 2024 11:21 |
Last Modified: |
15 Jan 2024 15:27 |
Publisher DOI: |
10.3233/SHTI230545 |
ARBOR DOI: |
10.24451/arbor.20913 |
URI: |
https://arbor.bfh.ch/id/eprint/20913 |
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