This thesis focuses on the development of novel data mining techniques that convert user interactions in social media networks into readable data that would benefit users, companies, and governments. The readable data can either warn of dangerous side effects of pharmaceutical drugs or improve intervention strategies. A weighted model enabled us to represent user activity in the network, that allowed us to reflect user sentiment of a pharmaceutical drug and/or service. The result is an accurate representation of user sentiment. This approach, when modified for specific diseases, drugs, and services, can enable rapid user feedback that can be converted into rapid responses from consumers to industry and government to withdraw possibly dangerous drugs and services from the market or improve said drugs and services.
Our approach monitors social media networks in real-time, enabling government and industry to rapidly respond to consumer sentiment of pharmaceutical drugs and services.
A novel data-mining method was developed to gauge the experiences of medical devices and drugs by patients with diabetes mellitus. Self-organizing maps were used to analyze forum posts numerically to better understand user opinion of medical devices and drugs. The end-result is a word list compilation that correlates certain positive and negative word cluster groups with medical drugs and devices. The implication of this novel data-mining method could open new avenues of research into rapid data collection, feedback, and analysis that would enable improved outcomes and solutions for public health.
A novel data mining method was developed to gauge the experience of the drug Sitagliptin (trade name Januvia) by patients with diabetes mellitus type 2. To this goal, we devised a two-step analysis framework. Initial exploratory analysis using self-organizing maps was performed to determine structures based on user opinions among the forum posts. The results were a compilation of user's clusters and their correlated (positive or negative) opinion of the drug. Subsequent modeling using network analysis methods was used to determine influential users among the forum members. These findings can open new avenues of research into rapid data collection, feedback, and analysis that can enable improved outcomes and solutions for public health and important feedback for the manufacturer.
A novel data-mining method was developed to gauge the experiences of the diabetes mellitus drug Januvia. Self-organizing maps were used to analyze forum posts numerically to infer user opinion of drug Januvia. Graph theory was used to discover influential users. The result is a word list compilation correlating positive and negative word cluster groups and a web of influential users on Januvia. The implications could open new research avenues into rapid data collection, feedback, and analysis that would enable improved solutions for public health.
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.
A novel data-mining method was developed to gauge the experiences of the oncology drug Tarceva. Self-organizing maps were used to analyze forum posts numerically to infer user opinion of drug Tarceva. The result is a word list compilation correlating positive and negative word cluster groups and a web of influential users on Tarceva. The implica-tions could open new research avenues into rapid data collec-tion, feedback, and analysis that would enable improved solu-tions for public health.
Depression is a global health concern. Social networks allow the affected population to share their experiences. These experiences, when mined, extracted, and analyzed, can be converted into either warnings to recall drugs (dangerous side effects), or service improvement (interventions, treatment options) based on observations derived from user behavior in depression-related social networks. Our aim was to develop a weighted network model to represent user activity on social health networks. This enabled us to accurately represent user interactions by relying on the data's semantic content. Our three-step method uses the weighted network model to represent user's activity, and network clustering and module analysis to characterize user interactions and extract further knowledge from user's posts. The network's topological properties reflect user activity such as posts' general topic as well as timing, while weighted edges reflect the posts semantic content and similarities among posts. The result, a synthesis from word data frequency, statistical analysis of module content, and the modeled health network's properties, has allowed us to gain insight into consumer sentiment of antidepressants. This approach will allow all parties to participate in improving future health solutions of patients suffering from depression.
Advances in information technology (IT) and big data are affecting nearly every facet of the public and private sectors. Social media platforms are one example of such advances: its nature allows users to connect, collaborate, and debate on any topic with comparative ease. The result is a hefty volume of user-generated content that, if properly mined and analyzed, could help the public and private health care sectors improve the quality of their products and services while reducing costs. The users of these platforms are the key to these improvements, as their valuable feedback will help improve health solutions.
Point-of-care (POC) diagnostics is playing an increasingly important role in public health, environmental monitoring, and food safety analysis. Smartphones, alone or in conjunction with add-on devices, have shown great capability of data collection, analysis, display, and transmission, making them popular in POC diagnostics. In this article, the state-ofthe- art advances in smartphone-based POC diagnostic technologies and their applications in the past few years are outlined, ranging from in vivo tests that use smartphone’s built-in/external sensors to detect biological signals to in vitro tests that involves complicated biochemical reactions. Novel techniques are illustrated by a number of attractive examples, followed by a brief discussion of the smartphone’s role in telemedicine. The challenges and perspectives of smartphonebased POC diagnostics are also provided.