Extracting health-related causality from twitter messages using natural language processing.

Journal: BMC medical informatics and decision making

PMID: 30943954

Abstract

BACKGROUND: Twitter messages (tweets) contain various types of topics in our daily life, which include health-related topics. Analysis of health-related tweets would help us understand health conditions and concerns encountered in our daily lives. In this paper we evaluate an approach to extracting causalities from tweets using natural language processing (NLP) techniques.

Authors

Son Doan

University of California San Diego, La Jolla, CA.
Elly W Yang

Medical Informatics, Kaiser Permanente Southern California, San Diego, CA.
Sameer S Tilak

Medical Informatics, Kaiser Permanente Southern California, San Diego, CA, 92130, USA.
Peter W Li

Medical Informatics, Kaiser Permanente Southern California, San Diego, CA, 92130, USA.
Daniel S Zisook

Medical Informatics, Kaiser Permanente Southern California, 11975 El Camino Real, Suite 105, San Diego, CA, United States.
Manabu Torii

Keywords

Causality Datasets as Topic Headache Humans Information Storage and Retrieval Natural Language Processing Sleep Initiation and Maintenance Disorders Social Media Stress, Psychological Text Messaging

External Resources

View on PubMed Access via DOI PubMed (30943954)

Extracting health-related causality from twitter messages using natural language processing.

Abstract

Authors

Keywords

External Resources

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