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

BMC Medical Informatics and Decision Making. 2019;19(S3):71-77 DOI 10.1186/s12911-019-0785-0

 

Journal Homepage

Journal Title: BMC Medical Informatics and Decision Making

ISSN: 1472-6947 (Online)

Publisher: BMC

LCC Subject Category: Medicine: Medicine (General): Computer applications to medicine. Medical informatics

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS

Son Doan (Medical Informatics, Kaiser Permanente Southern California)
Elly W. Yang (Medical Informatics, Kaiser Permanente Southern California)
Sameer S. Tilak (Medical Informatics, Kaiser Permanente Southern California)
Peter W. Li (Medical Informatics, Kaiser Permanente Southern California)
Daniel S. Zisook (Medical Informatics, Kaiser Permanente Southern California)
Manabu Torii (Medical Informatics, Kaiser Permanente Southern California)

EDITORIAL INFORMATION

Open peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 23 weeks

 

Abstract | Full Text

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. Methods Lexico-syntactic patterns based on dependency parser outputs are used for causality extraction. We focused on three health-related topics: “stress”, “insomnia”, and “headache.” A large dataset consisting of 24 million tweets are used. Results The results show the proposed approach achieved an average precision between 74.59 to 92.27% in comparisons with human annotations. Conclusions Manual analysis on extracted causalities in tweets reveals interesting findings about expressions on health-related topic posted by Twitter users.