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Health Information Extraction from Social Media

Abstract Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks such as pharmacovigilance via the use of Natural Language Processing (NLP) techniques. One of the critical steps in information extraction pipelines is Named Entity Recognition (NER), where the mentions of entities such as diseases are located in text and their entity type are identified. However, the language in social media is highly informal, and user-expressed health-related concepts are often non-technical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and advanced machine learning-based NLP techniques ... (more)
Created Date 2016
Contributor Nikfarjam, Azadeh (Author) / Gonzalez, Graciela (Advisor) / Greenes, Robert (Committee member) / Scotch, Matthew (Committee member) / Arizona State University (Publisher)
Subject Bioinformatics / Artificial intelligence / Public health / Deep Learning / Information Extraction / Machine Learning / Natural Language Processing / Pharmacovigilance / Social Media Mining
Type Doctoral Dissertation
Extent 105 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Biomedical Informatics 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis