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  • 1.
    Akay, Altug
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    A Novel Method to Intelligently Mine Social Media to Assess Consumer Sentiment of Pharmaceutical Drugs2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    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.

  • 2.
    Akay, Altug
    et al.
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    Dragomir, A.
    Department of Biomedical Engineering, University of Houston, Houston, TX, US.
    Erlandsson, Björn-Erik
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    A novel data-mining approach leveraging social media to monitor and respond to outcomes of diabetes drugs and treatment2013In: 2013 IEEE Point-of-Care Healthcare Technologies (PHT), New York: IEEE , 2013, p. 264-266Conference paper (Refereed)
    Abstract [en]

    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.

  • 3.
    Akay, Altug
    et al.
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    Dragomir, A
    Erlandsson, Björn-Erik
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    A Novel Data-Mining Approach Leveraging Social Media to Monitor Consumer Opinion of Sitagliptin2015In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 19, no 1, p. 389-396Article in journal (Refereed)
    Abstract [en]

    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.

  • 4.
    Akay, Altug
    et al.
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    Dragomir, A.
    Erlandsson, Björn-Erik
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    A novel data-mining platform leveraging social media to monitor outcomes of Januvia2013In: Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, IEEE conference proceedings, 2013, p. 7484-7487Conference paper (Refereed)
    Abstract [en]

    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.

  • 5.
    Akay, Altug
    et al.
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    Dragomir, A
    Erlandsson, Björn-Erik
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    Network-Based Modeling and Intelligent Data Mining of Social Media for Improving Care2015In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 19, no 1, p. 210-218Article in journal (Refereed)
    Abstract [en]

    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.

  • 6.
    Akay, Altug
    et al.
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    Dragomir, Andrei
    Erlandsson, Björn-Erik
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    A Novel-Data Mining Platform to Monitor the Outcomes of Erlontinib (Tarceva) using Social Media2014In: XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013, Springer, 2014, p. 1394-1397Conference paper (Refereed)
    Abstract [en]

    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.

  • 7.
    Akay, Altug
    et al.
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    Dragomir, Andrei
    Erlandsson, Björn-Erik
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    Assessing Antidepressants Using Intelligent Data Monitoring and Mining of Online Fora2016In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 20, no 4, p. 977-986Article in journal (Refereed)
    Abstract [en]

    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.

  • 8.
    Akay, Altug
    et al.
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    Dragomir, Andrei
    University of Houston, Biomedical Engineering.
    Erlandsson, Björn-Erik
    KTH, School of Technology and Health (STH), Health Systems Engineering.
    Mining Social Media Big Data for Health2015In: IEEE PulseArticle, review/survey (Refereed)
    Abstract [en]

    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.

  • 9.
    Xu, Xiayu
    et al.
    School of Life Science and Technology,Xián University, Xián, China.
    Akay, Altug
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    Wei, Huilin
    Wang, ShuQi
    Pingguan-Murphy, Belinda
    Erlandsson, Björn-Erik
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    Li, Xiujun
    Lee, Wongu
    Hu, Jie
    Wang, Lin
    Xu, Feng
    Advances in Smartphone-Based Point-of-Care Diagnostics: This paper reviews the state-of-the-art advances in smartphone-based point-of-care diagnostic technologies and their applications in medicine and biology.2015In: Proceedings of the IEEE, ISSN 0018-9219, E-ISSN 1558-2256, Vol. 103, no 2, p. 236-247Article in journal (Refereed)
    Abstract [en]

    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.

1 - 9 of 9
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