title: Network-based modeling and intelligent data mining of social media for improving care.
abstract: Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and reduce costs using consumer-generated opinion. We propose a two-step analysis framework that focuses on positive and negative sentiment, as well as the side effects of treatment, in users' forum posts, and identifies user communities (modules) and influential users for the purpose of ascertaining user opinion of cancer treatment. We used a self-organizing map to analyze word frequency data derived from users' forum posts. We then introduced a novel network-based approach for modeling users' forum interactions and employed a network partitioning method based on optimizing a stability quality measure. This allowed us to determine consumer opinion and identify influential users within the retrieved modules using information derived from both word-frequency data and network-based properties. Our approach can expand research into intelligently mining social media data for consumer opinion of various treatments to provide rapid, up-to-date information for the pharmaceutical industry, hospitals, and medical staff, on the effectiveness (or ineffectiveness) of future treatments.
authors.forename: Altug
authors.initials: A
authors.lastname: Akay
authors.forename: Andrei
authors.initials: A
authors.lastname: Dragomir
authors.forename: Bjorn-Erik
authors.initials: BE
authors.lastname: Erlandsson
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use; D001185:Artificial Intelligence; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods* / Q000706:statistics & numerical data; D000069347:Erlotinib Hydrochloride; D057220:Health Literacy / Q000706:statistics & numerical data; D006801:Humans; D008175:Lung Neoplasms / Q000188:drug therapy*; D062527:Meaningful Use / Q000706:statistics & numerical data; D015233:Models, Statistical; D009323:Natural Language Processing; D020380:Needs Assessment / Q000706:statistics & numerical data; D010353:Patient Education as Topic / Q000706:statistics & numerical data*; D017060:Patient Satisfaction / Q000706:statistics & numerical data*; D058996:Quality Improvement / Q000706:statistics & numerical data; D011799:Quinazolines / Q000627:therapeutic use*; D061108:Social Media / Q000706:statistics & numerical data*; D016896:Treatment Outcome; D018875:Vocabulary, Controlled
chemical_list: D000970:Antineoplastic Agents; D011799:Quinazolines; D000069347:Erlotinib Hydrochloride