Natural Language Processing Methods to Extract Lifestyle Exposures for Alzheimer's Disease from Clinical Notes

Yoonkwon Yi; Zitao Shen; Anusha Bompelli; Fang Yu; Yanshan Wang; Rui Zhang

doi:10.1109/ICHI48887.2020.9374320

Natural Language Processing Methods to Extract Lifestyle Exposures for Alzheimer's Disease from Clinical Notes

Yoonkwon Yi, Zitao Shen, Anusha Bompelli, Fang Yu, Yanshan Wang, Rui Zhang

Digital Health Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Due to the absence of medications on Alzheimer's disease (AD), lifestyle exposures that could improve cognitive functionality have become extremely important. Thus, the objective of the study was to show the feasibility of using natural language processing (NLP) methods to extract lifestyle exposures from clinical texts. The proposed named-entity recognition (NER) task's results indicate that NLP models can detect lifestyle information (i.e., excessive diet, physical activity, sleep deprivation and substance abuse) from clinical notes, which has the potential for improving efficiency in information acquisition and accrual for AD clinical trials.

Original language	English (US)
Title of host publication	2020 IEEE International Conference on Healthcare Informatics, ICHI 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728153827
DOIs	https://doi.org/10.1109/ICHI48887.2020.9374320
State	Published - Nov 2020
Event	8th IEEE International Conference on Healthcare Informatics, ICHI 2020 - Virtual, Oldenburg, Germany Duration: Nov 30 2020 → Dec 3 2020

Publication series

Name	2020 IEEE International Conference on Healthcare Informatics, ICHI 2020

Conference

Conference	8th IEEE International Conference on Healthcare Informatics, ICHI 2020
Country/Territory	Germany
City	Virtual, Oldenburg
Period	11/30/20 → 12/3/20

Keywords

Alzheimer's disease
Deep learning
Electronic health records
Information extraction
Lifestyle exposure
Machine learning
Natural language processing

ASJC Scopus subject areas

Computer Science Applications
Computer Vision and Pattern Recognition
Hardware and Architecture
Decision Sciences (miscellaneous)
Modeling and Simulation
Medicine (miscellaneous)
Health Informatics
Health(social science)

Access to Document

10.1109/ICHI48887.2020.9374320

Cite this

Yi, Y., Shen, Z., Bompelli, A., Yu, F., Wang, Y., & Zhang, R. (2020). Natural Language Processing Methods to Extract Lifestyle Exposures for Alzheimer's Disease from Clinical Notes. In 2020 IEEE International Conference on Healthcare Informatics, ICHI 2020 Article 9374320 (2020 IEEE International Conference on Healthcare Informatics, ICHI 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI48887.2020.9374320

Natural Language Processing Methods to Extract Lifestyle Exposures for Alzheimer's Disease from Clinical Notes. / Yi, Yoonkwon; Shen, Zitao; Bompelli, Anusha et al.
2020 IEEE International Conference on Healthcare Informatics, ICHI 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9374320 (2020 IEEE International Conference on Healthcare Informatics, ICHI 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yi, Y, Shen, Z, Bompelli, A, Yu, F, Wang, Y & Zhang, R 2020, Natural Language Processing Methods to Extract Lifestyle Exposures for Alzheimer's Disease from Clinical Notes. in 2020 IEEE International Conference on Healthcare Informatics, ICHI 2020., 9374320, 2020 IEEE International Conference on Healthcare Informatics, ICHI 2020, Institute of Electrical and Electronics Engineers Inc., 8th IEEE International Conference on Healthcare Informatics, ICHI 2020, Virtual, Oldenburg, Germany, 11/30/20. https://doi.org/10.1109/ICHI48887.2020.9374320

Yi Y, Shen Z, Bompelli A, Yu F, Wang Y, Zhang R. Natural Language Processing Methods to Extract Lifestyle Exposures for Alzheimer's Disease from Clinical Notes. In 2020 IEEE International Conference on Healthcare Informatics, ICHI 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9374320. (2020 IEEE International Conference on Healthcare Informatics, ICHI 2020). doi: 10.1109/ICHI48887.2020.9374320

Yi, Yoonkwon ; Shen, Zitao ; Bompelli, Anusha et al. / Natural Language Processing Methods to Extract Lifestyle Exposures for Alzheimer's Disease from Clinical Notes. 2020 IEEE International Conference on Healthcare Informatics, ICHI 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 IEEE International Conference on Healthcare Informatics, ICHI 2020).

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abstract = "Due to the absence of medications on Alzheimer's disease (AD), lifestyle exposures that could improve cognitive functionality have become extremely important. Thus, the objective of the study was to show the feasibility of using natural language processing (NLP) methods to extract lifestyle exposures from clinical texts. The proposed named-entity recognition (NER) task's results indicate that NLP models can detect lifestyle information (i.e., excessive diet, physical activity, sleep deprivation and substance abuse) from clinical notes, which has the potential for improving efficiency in information acquisition and accrual for AD clinical trials.",

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Natural Language Processing Methods to Extract Lifestyle Exposures for Alzheimer's Disease from Clinical Notes

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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