An Exploratory Study of Social Media Analysis for Rare Diseases Using Machine Learning Algorithms: A Case Study of Trigeminal Neuralgia
Files
Date
2020-01-07
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
Abstract
Rare diseases, affecting approximately 30 million Americans, are often poorly understood by clinicians due to lack of familiarity with the disease and proper research. Patients with rare diseases are often unfavorably treated, especially those with extremely painful chronic orofacial rare disorders. In the absence of structured knowledge, such patients often choose social media to seek help from peers within patient-oriented social media communities thereby generating tremendous amounts of unstructured data daily. We investigate whether we can organize this unstructured data using machine learning to help members of rare communities find relevant information more efficiently in real-time. We chose Trigeminal Neuralgia (TN), an extremely painful rare disorder, as our case study and collected 20,000 social media TN posts. We categorized TN posts into Twitter (very short), and Facebook (short, medium, long) datasets based on message length and performed three clustering experiments. Results revealed GSDMM outperformed both K-means and Spherical K-means in clustering Facebook especially for short messages in terms of speed. For long messages, MDS reduction outperformed the PCA when both were used with K-means and Spherical K-means. Our study demonstrated the need for further topic modeling to utilize among high level clusters based on semantic analysis of posts within each cluster.
Description
Keywords
Social Media and Healthcare Technology, clustering, machine learning, rare disease, social media, trigeminal neuralgia
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 53rd Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Collections
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.