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

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

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

6 Scopus citations

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

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

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

Publisher link

10.1109/ICHI48887.2020.9374320

Find It

Find It at U of M Libraries

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, Anu 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, Anu 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).

@inproceedings{b64f30a2898b4317bafdb5e32b3f01cb,

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

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.",

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

author = "Yoonkwon Yi and Zitao Shen and Anu Bompelli and Fang Yu and Yanshan Wang and Rui Zhang",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 8th IEEE International Conference on Healthcare Informatics, ICHI 2020 ; Conference date: 30-11-2020 Through 03-12-2020",

year = "2020",

month = nov,

doi = "10.1109/ICHI48887.2020.9374320",

language = "English (US)",

series = "2020 IEEE International Conference on Healthcare Informatics, ICHI 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 IEEE International Conference on Healthcare Informatics, ICHI 2020",

}

TY - GEN

T1 - Natural Language Processing Methods to Extract Lifestyle Exposures for Alzheimer's Disease from Clinical Notes

AU - Yi, Yoonkwon

AU - Shen, Zitao

AU - Bompelli, Anu

AU - Yu, Fang

AU - Wang, Yanshan

AU - Zhang, Rui

PY - 2020/11

Y1 - 2020/11

N2 - 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.

AB - 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.

KW - Alzheimer's disease

KW - Deep learning

KW - Electronic health records

KW - Information extraction

KW - Lifestyle exposure

KW - Machine learning

KW - Natural language processing

UR - https://www.scopus.com/pages/publications/85103205465

UR - https://www.scopus.com/pages/publications/85103205465#tab=citedBy

U2 - 10.1109/ICHI48887.2020.9374320

DO - 10.1109/ICHI48887.2020.9374320

M3 - Conference contribution

AN - SCOPUS:85103205465

T3 - 2020 IEEE International Conference on Healthcare Informatics, ICHI 2020

BT - 2020 IEEE International Conference on Healthcare Informatics, ICHI 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 8th IEEE International Conference on Healthcare Informatics, ICHI 2020

Y2 - 30 November 2020 through 3 December 2020

ER -

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

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Publisher link

Find It

Other files and links

Fingerprint

Cite this