Cross-Lingual Knowledge Transfer for Clinical Phenotyping

Jens-Michalis Papaioannou, Paul Grundmann, Betty van Aken, Athanasios Samaras, Ilias Kyparissidis, George Giannakoulas, Felix Gers, Alexander Loeser


Abstract
Clinical phenotyping enables the automatic extraction of clinical conditions from patient records, which can be beneficial to doctors and clinics worldwide. However, current state-of-the-art models are mostly applicable to clinical notes written in English. We therefore investigate cross-lingual knowledge transfer strategies to execute this task for clinics that do not use the English language and have a small amount of in-domain data available. Our results reveal two strategies that outperform the state-of-the-art: Translation-based methods in combination with domain-specific encoders and cross-lingual encoders plus adapters. We find that these strategies perform especially well for classifying rare phenotypes and we advise on which method to prefer in which situation. Our results show that using multilingual data overall improves clinical phenotyping models and can compensate for data sparseness.
Anthology ID:
2022.lrec-1.95
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
900–909
Language:
URL:
https://aclanthology.org/2022.lrec-1.95
DOI:
Bibkey:
Cite (ACL):
Jens-Michalis Papaioannou, Paul Grundmann, Betty van Aken, Athanasios Samaras, Ilias Kyparissidis, George Giannakoulas, Felix Gers, and Alexander Loeser. 2022. Cross-Lingual Knowledge Transfer for Clinical Phenotyping. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 900–909, Marseille, France. European Language Resources Association.
Cite (Informal):
Cross-Lingual Knowledge Transfer for Clinical Phenotyping (Papaioannou et al., LREC 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.lrec-1.95.pdf
Code
 neuron1682/cross-lingual-phenotype-prediction
Data
MIMIC-III