Co-occurring evidence discovery for COPD patients using natural language processing

Baechle, C; Agarwal, A; Behara, R; Zhu, X

Co-occurring evidence discovery for COPD patients using natural language processing

Baechle, C Agarwal, A Behara, R Zhu, X

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Publication Type:: Conference Proceeding
Citation:: 2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017, 2017, pp. 321 - 324
Issue Date:: 2017-04-11

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Field	Value	Language
dc.contributor.author	Baechle, C	en_US
dc.contributor.author	Agarwal, A	en_US
dc.contributor.author	Behara, R	en_US
dc.contributor.author	Zhu, X	en_US
dc.date.issued	2017-04-11	en_US
dc.identifier.citation	2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017, 2017, pp. 321 - 324	en_US
dc.identifier.isbn	9781509041794	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125682
dc.description.abstract	© 2017 IEEE. Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung disease that affects airflow to the lungs. Discovering the co-occurrence of COPD with other diseases and symptoms is invaluable to medical staff. Building co-occurrence indexes and finding causal relationships with COPD can be difficult because often times disease prevalence within a population influences results. A method which can better separate occurrence within COPD patients from population prevalence would be desirable. Natural Language Processing (NLP) methods are used to examine 64,371 deidentified clinical notes and discover associations between COPD and medical terms. A co-occurrence score is presented which can penalize scores based on term prevalence. The maximum improvements in recall for symptoms and diseases were 0.212 and 0.130. The maximum improvements in precision for symptoms and diseases were 0.303 and 0.333.	en_US
dc.relation.ispartof	2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017	en_US
dc.relation.isbasedon	10.1109/BHI.2017.7897270	en_US
dc.title	Co-occurring evidence discovery for COPD patients using natural language processing	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung disease that affects airflow to the lungs. Discovering the co-occurrence of COPD with other diseases and symptoms is invaluable to medical staff. Building co-occurrence indexes and finding causal relationships with COPD can be difficult because often times disease prevalence within a population influences results. A method which can better separate occurrence within COPD patients from population prevalence would be desirable. Natural Language Processing (NLP) methods are used to examine 64,371 deidentified clinical notes and discover associations between COPD and medical terms. A co-occurrence score is presented which can penalize scores based on term prevalence. The maximum improvements in recall for symptoms and diseases were 0.212 and 0.130. The maximum improvements in precision for symptoms and diseases were 0.303 and 0.333.

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http://hdl.handle.net/10453/125682