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Studies in Health Technology and Informatics

Anne Quesnel-Barbet, Julien Soula, Pierre Bazile, Eric-André Sauleau, Pierre Parrend
Geomatics becomes a major field of science facing challenges to assist medical informatics and health decision makers thanks to attractive concepts, methods and easy, user-friendly-way IT technologies. PoleSat_2018 presents a web-based graphical user interface with an embedded optimized and automated algorithm. It is primarily geared for geomatics non-specialists and allows computer simulations by modelling scenarios of hospital grouping and/or closure. The consultation, reflection, prospective views, offered in a very short time to policy makers will find a successful support for health planning strategic decisions...
2019: Studies in Health Technology and Informatics
Mert Baskaya, Mustafa Yuksel, Gokce Banu Laleci Erturkmen, Miriam Cunningham, Paul Cunningham
mHealth4Afrika has introduced the use of CE approved medical sensors at the point of care in primary healthcare facilities in Africa as part of an integrated platform supporting primary health care services. This paper shares insights into the standards-based architecture and HL7 FHIR service developed to support data transfer from sensors with proprietary standards to populate the mHealth4Afrika electronic patient record via custom Android and Windows applications. The current iteration is being validated in healthcare facilities in Ethiopia, Kenya, Malawi and South Africa...
2019: Studies in Health Technology and Informatics
Blanca Flores, Matthias Ganzinger, Aleksei Dudchenko, Petra Knaup
No abstract text is available yet for this article.
2019: Studies in Health Technology and Informatics
Parisis Gallos, Santiago Aso, Serge Autexier, Arturo Brotons, Antonio De Nigro, Gregor Jurak, Athanasios Kiourtis, Pavlos Kranas, Dimosthenis Kyriazis, Mitja Lustrek, Andrianna Magdalinou, Ilias Maglogiannis, John Mantas, Antonio Martinez, Andreas Menychtas, Lydia Montandon, Florin Picioroaga, Manuel Perez, Dalibor Stanimirovic, Gregor Starc, Tanja Tomson, Ruth Vilar-Mateo, Ana-Maria Vizitiu
The aim of this paper is to present examples of big data techniques that can be applied on Holistic Health Records (HHR) in the context of the CrowdHEALTH project. Real-time big data analytics can be performed on the stored data (i.e. HHRs) enabling correlations and extraction of situational factors between laboratory exams, physical activities, biosignals, medical data patterns, and clinical assessment. Based on the outcomes of different analytics (e.g. risk analysis, pathways mining, forecasting and causal analysis) on the aforementioned HHRs datasets, actionable information can be obtained for the development of efficient health plans and public health policies...
2019: Studies in Health Technology and Informatics
Francisco J Núñez-Benjumea, Jesús Moreno-Conde, Sara González-García, Alberto Moreno-Conde, José L López-Guerra, María J Ortiz-Gordillo, Carlos L Parra-Calderón
This work addresses a scoping review of Feature Selection (FS) methods applied to a Lung Cancer dataset to elucidate parameters' relevance when predicting radiotherapy (RT) induced toxicity. Subsetting-based and Ranking-based FS methods were implemented along with 4 advanced classifiers to predict the onset of RT-induced acute esophagitis, cough, pneumonitis and dyspnea. Their prediction performance was measured in terms of the AUC for each model to find the best FS.
2019: Studies in Health Technology and Informatics
Jonathan Krebs, Markus Krug, Georg Fette, Georg Dietrich, Maximilian Ertl, Gülmisal Güder, Frank Puppe, Mathias Kaspar
No abstract text is available yet for this article.
2019: Studies in Health Technology and Informatics
Simon de Lusignan, Nadia Smith, Valerie Livina, Ivelina Yonova, Rebecca Webb, Spencer A Thomas
The analysis of primary care data plays an important role in understanding health at an individual and population level. Currently the utilization of computerized medical records is low due to the complexities, heterogeneities and veracity associated with these data. We present a deep learning methodology that clusters 11,000 records in an unsupervised manner identifying non-linear patterns in the data. This provides a useful tool for visualization as well as identify features driving the formation of clusters...
2019: Studies in Health Technology and Informatics
Johanna Fiebeck, Matthias Gietzelt, Sarah Ballout, Martin Christmann, Maikel Fradziak, Hans Laser, Julia Ruppel, Norman Schönfeld, Sonja Teppner, Svetlana Gerbel
The Logical Observation Identifiers, Names and Codes (LOINC) is a common terminology used for standardizing laboratory terms. Within the HiGHmed consortium, LOINC is used as a central terminology for health data sharing across all university hospital sites. Therefore, linking the LOINC codes to the site-specific tests and measures is one crucial step to reach this goal. In this work we report our ongoing work in implementing LOINC to the laboratory information system, our challenges and lessons learned.
2019: Studies in Health Technology and Informatics
A Sargeant, T von Landesberger, C Baier, F Bange, A Dalpke, T Eckmanns, S Glöckner, M Kaase, G Krause, M Marschollek, B Malone, M Niepert, S Rey, A Wulff, S Scheithauer
Within the HiGHmed Project there are three medical use cases. The use cases include the scopes cardiology, oncology and infection. They serve to specify the requirements for the development and implementation of a local and federated platform, with the result that data from medical care and research should be retrievable, reusable and interchangeable. The Use Case Infection Control aims to establish an early detection of transmission events as well as clusters and outbreaks of various pathogens. Therefore the use case wants to establish the smart infection control system (SmICS)...
2019: Studies in Health Technology and Informatics
Ravi Dadsena, Rohini P, S Ramakrishnan
The brain ventricles are surrounded by periventricular structures that are affected by dementia which results in neurodegenerative disorder such as Alzheimer's Disease (AD). The change in morphology of these structures must effect the shape and volume of Corpus Callosum (CC). These alterations in morphology of CC are considered to be a significant image biomarker for the early diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) subjects. Shape descriptors provide useful information about change in morphology of various brain structures during disease progression...
2019: Studies in Health Technology and Informatics
Nicole Hechtel, Jörn Krückeberg, Michael Marschollek
To measure and compare the workload of nurses in a clinical setting raises up some questions. On the one hand we worked out which criteria can represent workload and how it can be measured. On the other hand we compile different requirements for wearable sensors. These requirements can be categorized in four groups: data, robustness, hygiene and usability. These results can support the selection of wearable sensors for a survey of the workload of nurses in a clinical setting.
2019: Studies in Health Technology and Informatics
Sarah Riepenhausen, Julian Varghese, Philipp Neuhaus, Michael Storck, Alexandra Meidt, Stefan Hegselmann, Martin Dugas
The Portal of Medical Data Models is an open-access platform for medical forms and data models. Annotation with UMLS codes enables semantic interoperability and secondary use of data. The number of forms and users are growing. The site has been updated and two analyzing tools have been added.
2019: Studies in Health Technology and Informatics
Marina Fotteler, Felix Holl, Christopher Käsbach, Jürgen Schlegel, Walter Swoboda
No abstract text is available yet for this article.
2019: Studies in Health Technology and Informatics
Sandra Kühnel, Milan Jovanovic, Henry Hoffmann, Sabrina Golde, Martin C Hirsch
Many current Clinical Decision Support Systems which assist clinical diagnosis, are based on a causal condition-symptom relation. To reach more diagnostic precision Ada's Deep Reasoning is substituting this approach with the use of a model based on pathophysiology.
2019: Studies in Health Technology and Informatics
Thomas Boillat, Nadeem Alduaij, Frédéric Ehrler
No abstract text is available yet for this article.
2019: Studies in Health Technology and Informatics
Arriel Benis, Almog Boim, Amos Notea
The aim of this initial research is to show that data and information collected from Internet Social Networks support the understanding of individual and collective behaviors which can help emergencies and disasters managers to mitigate and to improve preparedness programs for future similar events and to make more suitable decisions.
2019: Studies in Health Technology and Informatics
Christian Gulden, Inge Landerer, Azadeh Nassirian, Fatma Betül Altun, Johanna Andrae
Understanding the prevalence of structured data elements within clinical trial eligibility criteria is a crucial step for prioritizing integration efforts to supported automated patient recruitment into clinical trials based on electronic health record data. In this work, we extract data elements from 50 clinical trials using a collaborative, crowd-sourced, and iterative method. A total of 1.120 criteria were analyzed, and 204 unique data elements were extracted. The most prevalent elements were diagnosis code, procedure code, and medication code, occurring in 414 (37 %), 112 (10 %), and 91 (8 %) of eligibility criteria respectively...
2019: Studies in Health Technology and Informatics
Sattar Rahimbayli, Jens Lehmann, Thomas M Deserno
Following the new medical device regulations, vendors need to track their products. Unlike to clinical research, data entry in such registries is done by staff that basically needs to ensure resident's treatment. Enforcing data completeness while entering is not an option anymore. In this work, we suggest an independent pay for performance system, that connects via REST to the registries. It allows to define rules that check data consistency and completeness, and rewards for records that fulfill the requirement...
2019: Studies in Health Technology and Informatics
Hanna Hasselblatt, Johanna Andrae, Adrian Tassoni, Kai Fitzer, Thomas Bahls, Hans-Ulrich Prokosch, Martin Boeker
In the long-run we wish to demonstrate the power of linking clinical trial information to routine health records for straightforward patient recruitment - not only at each single hospital but in a large German consortium (called MIRACUM). In such architecture a hospital wide clinical trial registry (CTR) plays a major role. All such site specific CTR however, also need to be interoperable and support automated data provision for a central MIRACUM wide trial registry. Based on a survey of already existing trial information systems at each partner site and a comparison of their functionality, a joint requirement specification was created, a minimal MIRACUM wide trial core dataset was defined and an architecture was designed in which each MIRACUM partner could keep their autonomous system decision...
2019: Studies in Health Technology and Informatics
Matthias Löbe, Frank Meineke, Alfred Winter
Clinical Data Management Systems (CDMS) are used to electronically capture and store data about study participants in clinical trials. CDMS tend to be superior compared to paper-based data capture with respect to data quality, consistency, completeness and traceability. Nevertheless, their application is not default - especially in small-scale, academic clinical studies. While clinical researchers can choose from many different software vendors, the vast requirements of data management and the growing need for integration with other systems make it hard to select the most suitable one...
2019: Studies in Health Technology and Informatics
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