Leveraging Natural Language Processing and Clinical Notes for Dementia Detection

Ming Liu, Richard Beare, Taya Collyer, Nadine Andrew, Velandai Srikanth


Abstract
Early detection and automated classification of dementia has recently gained considerable attention using neuroimaging data and spontaneous speech. In this paper, we validate the possibility of dementia detection with in-hospital clinical notes. We collected 954 patients’ clinical notes from a local hospital and assign dementia/non-dementia labels to those patients based on clinical assessment and telephone interview. Given the labeled dementia data sets, we fine tune a ClinicalBioBERT based on some filtered clinical notes and conducted experiments on both binary and three class dementia classification. Our experiment results show that the fine tuned ClinicalBioBERT achieved satisfied performance on binary classification but failed on three class dementia classification. Further analysis suggests that more human prior knowledge should be considered.
Anthology ID:
2023.clinicalnlp-1.20
Volume:
Proceedings of the 5th Clinical Natural Language Processing Workshop
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, Anna Rumshisky
Venue:
ClinicalNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
150–155
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2023.clinicalnlp-1.20/
DOI:
10.18653/v1/2023.clinicalnlp-1.20
Bibkey:
Cite (ACL):
Ming Liu, Richard Beare, Taya Collyer, Nadine Andrew, and Velandai Srikanth. 2023. Leveraging Natural Language Processing and Clinical Notes for Dementia Detection. In Proceedings of the 5th Clinical Natural Language Processing Workshop, pages 150–155, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Leveraging Natural Language Processing and Clinical Notes for Dementia Detection (Liu et al., ClinicalNLP 2023)
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PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2023.clinicalnlp-1.20.pdf