Improving Care using Network-Based Modeling and Intelligent Data Mining of Social Media

Abstract

Cleverly extracting information from social media has recently attracted nice interest from the medication and Health science community to at an identical time improve health care outcomes and deflate prices victimization consumer-generated opinion. We've got an inclination to tend to propose a social dancing analysis framework that focuses on positive and negative sentiment, in addition as a result of the aspect effects of treatment, in users’ forum posts, and identi?es user communities (modules) and in?uential users for the aim of ascertaining user opinion of cancer treatment. We get a preference to tend to use a self-organizing map to investigate word frequency information derived from users’ forum posts. we've got an inclination to tend to then introduced a unique network-based approach for modeling users’ forum interactions and utilized a network partitioning technique supported optimizing a stability quality live. This allowed North American nation to work out shopper opinion and establish in?uential users at intervals the retrieved modules victimization data derived from each word-frequency information and network-based properties. Our approach will expand analysis into showing intelligence mining social media information for shopper opinion of assorted treatments to supply fast, up-to-date data for the pharmaceutical trade, hospitals, and medical employees, on the effectiveness (or ineffectiveness) of future treatments.