Bridging The Gap Between Healthcare Providers and Consumers: Extracting Features from Online Health Forum to Meet Social Needs of Patients using Network Analysis and Embedding

If you need an accessible version of this item, please submit a remediation request.
Date
2020-08
Language
American English
Embargo Lift Date
Department
Committee Chair
Degree
M.S.
Degree Year
2020
Department
School of Informatics
Grantor
Indiana University
Journal Title
Journal ISSN
Volume Title
Found At
Abstract

Chronic disease patients have to face many issues during and after their treatment. A lot of these issues are either personal, professional, or social in nature. It may so happen that these issues are overlooked by the respective healthcare providers and become major obstacles in the patient’s day-to-day life and their disease management. We extract data from an online health platform that serves as a ‘safe haven’ to the patients and survivors to discuss help and coping issues. This thesis presents a novel approach that acts as the first step to include the social issues discussed by patients on online health forums which the healthcare providers need to consider in order to create holistic treatment plans. There are numerous online forums where patients share their experiences and post questions about their treatments and their subsequent side effects. We collected data from an “Online Breast Cancer Forum”. On this forum, users (patients) have created threads across many related topics and shared their experiences and questions. We connect the patients (users) with the topic in which they have posted by converting the data into a bipartite network and turn the network nodes into a high-dimensional feature space. From this feature space, we perform community detection on the node embeddings to unearth latent connections between patients and topics. We claim that these latent connections, along with the existing ones, will help to create a new knowledge base that will eventually help the healthcare providers to understand and acknowledge the non-medical related issues to a treatment, and create more adaptive and personalized plans. We performed both qualitative and quantitative analysis on the obtained embeddings to prove the superior quality of our approach and its potential to extract more information when compared to other models.

Description
Indiana University-Purdue University Indianapolis (IUPUI)
item.page.description.tableofcontents
item.page.relation.haspart
Cite As
ISSN
Publisher
Series/Report
Sponsorship
Major
Extent
Identifier
Relation
Journal
Source
Alternative Title
Type
Thesis
Number
Volume
Conference Dates
Conference Host
Conference Location
Conference Name
Conference Panel
Conference Secretariat Location
Version
Full Text Available at
This item is under embargo {{howLong}}