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  • 1.
    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.

  • 2.
    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.

  • 3.
    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.

  • 4.
    Ferreira, Javier
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering. Högskolan i Borås.
    Pau de la Cruz, Ivan
    Technical University of Madrid.
    Lindecrantz, Kaj
    KTH, School of Technology and Health (STH), Medical Engineering.
    Seoane, Fernando
    KTH, School of Technology and Health (STH), Medical Engineering. Högskolan i Borås, Akademin för vård, arbetsliv och välfärd.
    A handheld and textile-enabled bioimpedance system for ubiquitous body composition analysis.: An initial functional validation2016In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208Article in journal (Refereed)
    Abstract [en]

    In recent years, many efforts have been made to promote a healthcare paradigm shift from the traditional reactive hospital-centered healthcare approach towards a proactive, patient-oriented and self-managed approach that could improve service quality and help reduce costs while contributing to sustainability. Managing and caring for patients with chronic diseases accounts over 75% of healthcare costs in developed countries. One of the most resource demanding diseases is chronic kidney disease (CKD), which often leads to a gradual and irreparable loss of renal function, with up to 12% of the population showing signs of different stages of this disease. Peritoneal dialysis and home haemodialysis are life-saving home-based renal replacement treatments that, compared to conventional in-center hemodialysis, provide similar long-term patient survival, less restrictions of life-style, such as a more flexible diet, and better flexibility in terms of treatment options and locations. Bioimpedance has been largely used clinically for decades in nutrition for assessing body fluid distributions. Moreover, bioimpedance methods are used to assess the overhydratation state of CKD patients, allowing clinicians to estimate the amount of fluid that should be removed by ultrafiltration. In this work, the initial validation of a handheld bioimpedance system for the assessment of body fluid status that could be used to assist the patient in home-based CKD treatments is presented. The body fluid monitoring system comprises a custom-made handheld tetrapolar bioimpedance spectrometer and a textile-based electrode garment for total body fluid assessment. The system performance was evaluated against the same measurements acquired using a commercial bioimpedance spectrometer for medical use on several voluntary subjects. The analysis of the measurement results and the comparison of the fluid estimations indicated that both devices are equivalent from a measurement performance perspective, allowing for its use on ubiquitous e-healthcare dialysis solutions.

  • 5.
    Hafid, Abdelakram
    et al.
    Univ Sci & Technol Houari Boumediene, Lab Instrumentat, Algiers 16111, Algeria.;Univ Boras, Dept Text Technol, S-50332 Boras, Sweden..
    Benouar, Sara
    Univ Sci & Technol Houari Boumediene, Lab Instrumentat, Algiers 16111, Algeria.;Univ Boras, Dept Text Technol, S-50332 Boras, Sweden..
    Kedir-talha, Malika
    Univ Sci & Technol Houari Boumediene, Lab Instrumentat, Algiers 16111, Algeria..
    Abtahi, Farhad
    KTH, School of Technology and Health (STH). Karolinska Inst, Inst Environm Med, SE-17177 Stockholm, Sweden.;Karolinska Univ Hosp, Dept Clin Physiol, S-14186 Huddinge, Sweden..
    Attari, Mokhtar
    Univ Sci & Technol Houari Boumediene, Lab Instrumentat, Algiers 16111, Algeria..
    Seoane, Fernando
    Univ Boras, Swedish Sch Text, S-50190 Boras, Sweden.;Karolinska Inst, Dept Clin Sci Intervent & Technol, S-14186 Stockholm, Sweden.;Karolinska Univ Hosp, Dept Biomed Engn, S-14186 Stockholm, Sweden..
    Full Impedance Cardiography Measurement Device Using Raspberry PI3 and System-on-Chip Biomedical Instrumentation Solutions2018In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 22, no 6, p. 1883-1894Article in journal (Refereed)
    Abstract [en]

    Impedance cardiography (ICG) is a noninvasive method for monitoring cardiac dynamics using electrical bioimpedance (EBI) measurements. Since its appearance more than 40 years ago, ICG has been used for assessing hemodynamic parameters. This paper presents a measurement system based on two System on Chip (SoC) solutions and Raspberry PI, implementing both a full three-lead ECG recorder and an impedance cardiographer, for educational and research development purposes. Raspberry PI is a platform supporting Do-I t-Yourself project and education applications across the world. The development is part of Biosignal PI, an open hardware platform focusing in quick prototyping of physiological measurement instrumentation. The SoC used for sensing cardiac biopotential is the ADAS1000, and for the EBI measurement is the AD5933. The recordings were wirelessly transmitted through Bluetooth to a PC, where the waveforms were displayed, and hemodynamic parameters such as heart rate, stroke volume, ejection time and cardiac output were extracted from the ICG and ECG recordings. These results show how Raspberry PI can be used for quick prototyping using relatively widely available and affordable components, for supporting developers in research and engineering education. The design and development documents will be available on www.BiosignalPl.com, for open access under a Non Commercial-Share A like 4.0 International License.

  • 6.
    Yang, Geng
    et al.
    Zhejiang Univ, Coll Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310058, Zhejiang, Peoples R China..
    Jiang, Mingzhe
    Univ Turku, Dept Future Technol, SF-20500 Turku, Finland..
    Ouyang, Wei
    Inst Pasteur, Imaging & Modeling Unit, F-75015 Paris, France..
    Ji, Guangchao
    KTH, School of Technology and Health (STH).
    Xie, Haibo
    Zhejiang Univ, Coll Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310058, Zhejiang, Peoples R China..
    Rahmani, Amir M.
    Univ Calif Irvine, Dept Comp Sci, Irvine, CA 92697 USA.;TU Wien, Inst Comp Technol, A-1040 Vienna, Austria..
    Liljeberg, Pasi
    Univ Turku, Dept Future Technol, SF-20500 Turku, Finland..
    Tenhunen, Hannu
    KTH, School of Technology and Health (STH). Univ Turku, Dept Future Technol, SF-20500 Turku, Finland..
    IoT-Based Remote Pain Monitoring System: From Device to Cloud Platform2018In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 22, no 6, p. 1711-1719Article in journal (Refereed)
    Abstract [en]

    Facial expressions are among behavioral signs of pain that can be employed as an entry point to develop an automatic human pain assessment tool. Such a tool can be an alternative to the self-report method and particularly serve patients who are unable to self-report like patients in the intensive care unit and minors. In this paper, a wearable device with a biosensing facial mask is proposed to monitor pain intensity of a patient by utilizing facial surface electromyogram (sEMG). The wearable device works as a wireless sensor node and is integrated into an Internet of Things (IoT) system for remote pain monitoring. In the sensor node, up to eight channels of sEMG can be each sampled at 1000 Hz, to cover its full frequency range, and transmitted to the cloud server via the gateway in real time. In addition, both low energy consumption and wearing comfort are considered throughout the wearable device design for long-term monitoring. To remotely illustrate real-time pain data to caregivers, a mobile web application is developed for real-time streaming of high-volume sEMG data, digital signal processing, interpreting, and visualization. The cloud platform in the system acts as a bridge between the sensor node and web browser, managing wireless communication between the server and the web application. In summary, this study proposes a scalable IoT system for real-time biopotential monitoring and a wearable solution for automatic pain assessment via facial expressions.

1 - 6 of 6
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