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

被引:40
作者
Akay, Altug [1 ]
Dragomir, Andrei [2 ]
Erlandsson, Bjorn-Erik [1 ]
机构
[1] Sch Technol & Hlth, Royal Inst Technol, SE-14152 Stockholm, Sweden
[2] Univ Houston, Dept Biomed Engn, Houston, TX 77204 USA
关键词
Datamining; complex networks; neural networks; semantic web; social computing; RESISTANCE;
D O I
10.1109/JBHI.2014.2336251
中图分类号
TP [自动化技术、计算机技术];
学科分类号
080201 [机械制造及其自动化];
摘要
Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve health-care 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 bothword-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.
引用
收藏
页码:210 / 218
页数:9
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