@inproceedings{liu-etal-2023-leveraging,
title = "Leveraging Natural Language Processing and Clinical Notes for Dementia Detection",
author = "Liu, Ming and
Beare, Richard and
Collyer, Taya and
Andrew, Nadine and
Srikanth, Velandai",
editor = "Naumann, Tristan and
Ben Abacha, Asma and
Bethard, Steven and
Roberts, Kirk and
Rumshisky, Anna",
booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.clinicalnlp-1.20",
doi = "10.18653/v1/2023.clinicalnlp-1.20",
pages = "150--155",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Leveraging Natural Language Processing and Clinical Notes for Dementia Detection
%A Liu, Ming
%A Beare, Richard
%A Collyer, Taya
%A Andrew, Nadine
%A Srikanth, Velandai
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Rumshisky, Anna
%S Proceedings of the 5th Clinical Natural Language Processing Workshop
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F liu-etal-2023-leveraging
%X 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.
%R 10.18653/v1/2023.clinicalnlp-1.20
%U https://aclanthology.org/2023.clinicalnlp-1.20
%U https://doi.org/10.18653/v1/2023.clinicalnlp-1.20
%P 150-155
Markdown (Informal)
[Leveraging Natural Language Processing and Clinical Notes for Dementia Detection](https://aclanthology.org/2023.clinicalnlp-1.20) (Liu et al., ClinicalNLP 2023)
ACL