Investigating anatomical bias in clinical machine learning algorithms

Jannik Pedersen, Martin Laursen, Pernille Vinholt, Anne Alnor, Thiusius Savarimuthu


Abstract
Clinical machine learning algorithms have shown promising results and could potentially be implemented in clinical practice to provide diagnosis support and improve patient treatment. Barriers for realisation of the algorithms’ full potential include bias which is systematic and unfair discrimination against certain individuals in favor of others. The objective of this work is to measure anatomical bias in clinical text algorithms. We define anatomical bias as unfair algorithmic outcomes against patients with medical conditions in specific anatomical locations. We measure the degree of anatomical bias across two machine learning models and two Danish clinical text classification tasks, and find that clinical text algorithms are highly prone to anatomical bias. We argue that datasets for creating clinical text algorithms should be curated carefully to isolate the effect of anatomical location in order to avoid bias against patient subgroups.
Anthology ID:
2023.findings-eacl.103
Volume:
Findings of the Association for Computational Linguistics: EACL 2023
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1398–1410
Language:
URL:
https://aclanthology.org/2023.findings-eacl.103
DOI:
10.18653/v1/2023.findings-eacl.103
Bibkey:
Cite (ACL):
Jannik Pedersen, Martin Laursen, Pernille Vinholt, Anne Alnor, and Thiusius Savarimuthu. 2023. Investigating anatomical bias in clinical machine learning algorithms. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1398–1410, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
Investigating anatomical bias in clinical machine learning algorithms (Pedersen et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-eacl.103.pdf
Dataset:
 2023.findings-eacl.103.dataset.zip
Video:
 https://aclanthology.org/2023.findings-eacl.103.mp4