Publications

959 Publications visible to you, out of a total of 959

Abstract (Expand)

Many healthcare IT systems in Germany are unable to interoperate with other systems through standardised data formats. Therefore it is difficult to store and retrieve data and to establish a systematic collection of data with provenance across systems and even healthcare institutions. We outline the concept for a Transformation Pipeline that can act as a processor for proprietary medical data formats from multiple sources. Through a modular construction, the pipeline relies on different data extraction and data enrichment modules as well as on interfaces to external definitions for interoperability standards. The developed solution is extendable and reusable, enabling data transformation independent from current format definitions and entailing the opportunity of collaboration with other research groups.

Authors: Lo An Phan-Vogtmann, A. Helborn, Henner M. Kruse, E. Thomas, Andrew J. Heidel, K. Saleh, F. Rissner, M. Specht, A. Henkel, A. Scherag, D. Ammon

Date Published: 2019

Publication Type: Journal article

Abstract

Not specified

Authors: B. Schreiweis, D. Ammon, M. Sedlmayr, F. Albashiti, T. Wendt

Date Published: 2019

Publication Type: InBook

Abstract

Not specified

Authors: S. Zabka, D. Ammon, T. Ganslandt, J. E. Gewehr, C. Haverkamp, S. Kiefer, H. Lautenbacher, Matthias Löbe, S. Thun, M. Boeker

Date Published: 2019

Publication Type: InProceedings

Abstract (Expand)

BACKGROUND A significant proportion of patients develop left ventricular (LV) remodeling leading to heart failure after acute myocardial infarction (AMI). Being able to identify these patients wouldd represent a step forward towards personalized medicine. The present study aimed to determine the ability of cyclin dependent kinase inhibitor 1C (CDKN1C) to risk stratify AMI patients, in a sex-specific manner. METHODS CDKN1C expression was measured in blood samples obtained at admission in a test cohort of 447 AMI patients and a validation cohort of 294 patients. The study end-point was LV function assessed by the ejection fraction (EF) at follow-up. RESULTS In the test cohort, CDKN1C was lower in patients with a reduced EF (\textless40%) compared to patients with preserved EF (\geq50%). This observation was specific to women. CDKN1C was a significant univariate predictor of LV function in women only. In multivariable analysis including demographic and clinical parameters, CDKN1C predicted LV function in women (odds ratio [95% confidence interval] 0.44 [0.23-0.82]) but not in men (0.90 [0.70-1.16]). Addition of CDKN1C to a multivariable clinical model reduced the Akaike information criterion, attesting for an incremental predictive value, in women (p = 0.006) but not in men (p = 0.41). Bootstrap internal validation confirmed the added value of CDKN1C in women. The female-specific predictive value of CDKN1C was validated in the independent cohort. CONCLUSION CDKN1C is a novel female-specific biomarker of LV function after AMI.

Authors: Torkia Lalem, Lu Zhang, Markus Scholz, Ralph Burkhardt, Victoria Saccheti, Andrej Teren, Joachim Thiery, Yvan Devaux

Date Published: 2019

Publication Type: Journal article

Abstract

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Authors: Paul Schmücker, Ulrike Schemman, Alfred Winter, Oliver Bott, Petra Knaup-Gregori, Stefan Kropf, Heinrich Lautenbacher, An Lo Phan-Vogtmann, André Scherag, Cord Spreckelsen

Date Published: 2019

Publication Type: Journal article

Abstract

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Authors: Sebastian Stäubert, Alexander Strübing, J. Drepper, B. Bergh, Alfred Winter, A. Merzweiler

Date Published: 2019

Publication Type: InCollection

Abstract (Expand)

Business informatics and medical informatics adopt and adapt methods and knowledge from computer science and further develop appropriate methods for the particular needs in their application domains. A panel discussion at the 2018 conference of the German Society for Medical Informatics, Biometry and Epidemiology (GMDS) analyzed the relationship between business informatics, medical informatics and computer science. Five questions guided the discussion: What are the basic goals of these disciplines? To what extent does practical application of results shape the disciplines? Do medicine and economy demand for particular methods in in - for - mat - ics and computer science? How important is foundation by theory and evidence? Can the disciplines learn from each other? The analysis made clear that business informatics, medical informatics and computer science would gain profit from a more systematic mutual exchange. The \grqqLearning Healthcare System” could provide a useful framework. Wirtschaftsinformatik und Medizinische Informatik gehören zu den sogenannten Bindestrich-Informatik-Fächern, die sich mit der Anwendung der Methoden und Erkenntnisse der Informatik, aber auch mit der Weiterentwicklung solcher Methoden und Erkenntnisse für gewisse Anwendungsgebiete befassen. Auf einer Podiumsdiskussion der Jahrestagung 2018 der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS) wurde für Wirtschaftsinformatik, Medizinische Informatik und Informatik analysiert wie sie zueinander stehen. Die Analyse erfolgte anhand von fünf Fragen: Welche grundlegenden Ziele bestimmen die jeweilige wissenschaftliche Arbeit? Wie ist der Praxisbezug ausgeprägt? Inwieweit sind Besonderheiten von Medizin bzw. Ökonomie prägend für die jeweilige wissenschaftliche Arbeit? Welche Rolle spielen Theoriefundierung und Evidenz? Was können Wirtschaftsinformatik und Informatik von Medizinischer Informatik und Medizin lernen – und umgekehrt? Die Analyse zeigt, dass die drei Disziplinen von einem systematischen wechselseitigen Austausch profitieren können. Das \glqqLernende Gesundheitssystem\grqq bietet Ansätze für einen entsprechenden Rahmen.

Authors: Alfred Winter, Reinhold Haux, Barbara Paech, Frank Teuteberg, Ursula Hübner

Date Published: 2019

Publication Type: Journal article

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