@inproceedings{1148, abstract = {{The individual search for information about physicians on Web 2.0 platforms can affect almost all aspects of our lives. People can directly access physician rating websites via web browsers or use any search engine to find physician reviews and ratings filtered by location resp. specialty. However, sometimes keyword search does not meet user needs because of the disagreement of users’ common terms queries for symptoms and the widespread medical terminology. In this paper, we present the prototype of a specialised search engine that overcomes this by indexing user-generated content (i.e., review texts) for physician discovery and provides automatic suggestions as well as an appropriate visualisation. On the one hand, we consider the available numeric physician ratings as sorting criterion for the ranking of query results. Furthermore, we extended existing ranking algorithms with respect to domain-specific types and physicians ratings on the other hand. We gathered more than 860,000 review texts and collected more than 213,000 physician records. A random test shows that about 19.7% of 5,100 different words in total are health- related and partly belong to consumer health vocabularies. Our evaluation results show that the query results fit user's particular health issues when seeking for physicians.}}, author = {{Bäumer, Frederik Simon and Dollmann, Markus and Geierhos, Michaela}}, booktitle = {{The 6th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2015) / The 5th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH-2015) / Affiliated Workshops}}, editor = {{Shakshuki, Elhadi M.}}, issn = {{18770509}}, keywords = {{Physician Discovery, Consumer Health Vocabulary, Common Terms Query}}, location = {{Berlin, Germany}}, pages = {{417--424}}, publisher = {{Elsevier}}, title = {{{Find a Physician by Matching Medical Needs described in your Own Words}}}, doi = {{10.1016/j.procs.2015.08.362}}, volume = {{63}}, year = {{2015}}, } @inbook{1149, abstract = {{The contacts a health care provider (HCP), like a physician, has to other HCPs is perceived as a quality characteristic by patients. So far, only the German physician rating website jameda.de gives information about the interconnectedness of HCPs in business networks. However, this network has to be maintained manually and is thus incomplete. We therefore developed a system for uncovering latent connectivity of HCPs in online reviews to provide users with more valuable information about their HCPs. The overall goal of this approach is to extend already existing business networks of HCPs by integrating connections that are newly discovered by our system. Our most recent evaluation results are promising: 70.8 % of the connections extracted from the reviews texts were correctly identified and in total 3,788 relations were recognized that have not been displayed in jameda.de’s network before.}}, author = {{Bäumer, Frederik Simon and Geierhos, Michaela and Schulze, Sabine}}, booktitle = {{Information and Software Technologies. 21st International Conference, ICIST 2015, Druskininkai, Lithuania, October 15-16, 2015. Proceedings}}, editor = {{Dregvaite, Giedre and Damasevicius, Robertas}}, isbn = {{978-3-319-24769-4}}, keywords = {{Latent Connectivity, Person Named Entity Recognition and Disambiguation, Health Care Provider Reviews}}, location = {{Druskininkai, Lithuania}}, pages = {{3--15}}, publisher = {{Springer}}, title = {{{A System for Uncovering Latent Connectivity of Health Care Providers in Online Reviews}}}, doi = {{10.1007/978-3-319-24770-0_1}}, volume = {{538}}, year = {{2015}}, } @inbook{1150, abstract = {{Patients 2.0 increasingly inform themselves about the quality of medical services on physician rating websites. However, little is known about whether the reviews and ratings on these websites truly reflect the quality of services or whether the ratings on these websites are rather influenced by patients’ individual rating behavior. Therefore, we investigate more than 790,000 physician reviews on Germany’s most used physician rating website jameda.de. Our results show that patients’ ratings do not only reflect treatment quality but are also influenced by treatment quality independent factors like age and complaint behavior. Hence, we provide evidence that users should be well aware of user specific rating distortions when intending to make their physician choice based on these ratings.}}, author = {{Geierhos, Michaela and Bäumer, Frederik Simon and Schulze, Sabine and Klotz, Caterina}}, booktitle = {{Modeling and Using Context. 9th International and Interdisciplinary Conference, CONTEXT 2015, Lanarca, Cyprus, November 2-6, 2015. Proceedings}}, editor = {{Christiansen, Henning and Stojanovic, Isidora and Papadopoulos, George A.}}, isbn = {{9783319255903}}, keywords = {{Health 2.0, Rating Behavior, Patient Opinion Mining on Physician Rating Websites}}, location = {{Larnaca, Cyprus}}, pages = {{159--171}}, publisher = {{Springer}}, title = {{{Understanding the Patient 2.0: Gaining Insight into Patients' Rating Behavior by User-generated Physician Review Mining}}}, doi = {{10.1007/978-3-319-25591-0_12}}, volume = {{9405}}, year = {{2015}}, } @inproceedings{9879, abstract = {{Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5\% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge.}}, author = {{Kimotho, James Kuria and Meyer, Tobias and Sextro, Walter}}, booktitle = {{Prognostics and Health Management (PHM), 2014 IEEE Conference on}}, keywords = {{ageing, particle filtering (numerical methods), proton exchange membrane fuel cells, remaining life assessment, PEM fuel cell prognostics, PHM, RUL predictions, accelerated degradation, adaptive particle filter algorithm, dynamic loading, model parameter adaptation, prognostics and health management, proton exchange membrane fuel cells, remaining useful life estimation, self-healing effect, Adaptation models, Data models, Degradation, Estimation, Fuel cells, Mathematical model, Prognostics and health management}}, pages = {{1--6}}, title = {{{PEM fuel cell prognostics using particle filter with model parameter adaptation}}}, doi = {{10.1109/ICPHM.2014.7036406}}, year = {{2014}}, } @inproceedings{13317, abstract = {{Along with the increasing popularity of social media and online communities in many business settings, the notion of online community health has become a common means by which community managers judge the condition or state of their communities. It has also been introduced to the literature, yet the concept remains underspecified and fragmented. In this paper, we work toward a construct conceptualization of online community health. Through a review of extant literature and dialogue with specialists in the field, we develop a multi-dimensional construct of online community health, consisting of seven elements. In writing this paper, we attempt to foster theory development around new organizational forms by advancing a new and important construct. The paper further provides guidance to the managers of social media and online communities by taking a systematic look at the well-being of their communities.}}, author = {{Wagner, David and Richter, Alexander and Trier, Matthias and Wagner, Heinz-Theo}}, booktitle = {{Proceedings of the 35th International Conference on Information Systems. ICIS 2014}}, editor = {{Karahanna, Elena and Srinivasan, Ananth and Tan, Bernard}}, keywords = {{Social media, Online communities, Online community success, Online community health, Construct conceptualization}}, publisher = {{Association for Information Systems. AIS Electronic Library (AISeL)}}, title = {{{Towards a Conceptualization of Online Community Health}}}, year = {{2014}}, } @article{33398, author = {{Mulder, E. and Clément, G. and Linnarsson, D. and Paloski, W. H. and Wuyts, F. P. and Zange, J. and Frings-Meuthen, P. and Johannes, B. and Shushakov, V. and Grunewald, M. and Maassen, N. and Bühlmeier, Judith and Rittweger, J.}}, issn = {{1439-6319}}, journal = {{European Journal of Applied Physiology}}, keywords = {{Physiology (medical), Public Health, Environmental and Occupational Health, Orthopedics and Sports Medicine, General Medicine, Public Health, Environmental and Occupational Health, Physiology}}, number = {{4}}, pages = {{727--738}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Musculoskeletal effects of 5 days of bed rest with and without locomotion replacement training}}}, doi = {{10.1007/s00421-014-3045-0}}, volume = {{115}}, year = {{2014}}, } @article{34315, author = {{Gad, Mohamed Z. and Hassanein, Sally I. and Abdel-Maksoud, Sahar M. and Shaban, Gamal M. and Abou-Aisha, Khaled and Elgabarty, Hossam}}, issn = {{1354-750X}}, journal = {{Biomarkers}}, keywords = {{Health, Toxicology and Mutagenesis, Clinical Biochemistry, Biochemistry}}, number = {{8}}, pages = {{746--752}}, publisher = {{Informa UK Limited}}, title = {{{Assessment of serum levels of asymmetric dimethylarginine, symmetric dimethylarginine andl-arginine in coronary artery disease}}}, doi = {{10.3109/1354750x.2010.519784}}, volume = {{15}}, year = {{2010}}, }