Network-Based Modeling and Intelligent Data Mining of Social Media for Improving Care - 2015
Intelligently extracting information from social media has recently attracted nice interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and cut back costs using consumer-generated opinion. We propose a two-step analysis framework that focuses on positive and negative sentiment, further 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 have a tendency to used a self-organizing map to investigate word frequency knowledge derived from users' forum posts. We have a tendency to then introduced a novel network-primarily based approach for modeling users' forum interactions and utilized a network partitioning method primarily based on optimizing a stability quality live. This allowed us to determine shopper opinion and establish influential users among the retrieved modules using data derived from both word-frequency data and network-based properties. Our approach will expand analysis into intelligently mining social media knowledge for consumer opinion of varied treatments to produce speedy, up-to-date data for the pharmaceutical industry, hospitals, and medical employees, on the effectiveness (or ineffectiveness) of future treatments.
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