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958 Publications visible to you, out of a total of 958
POLAR – „POLypharmazie, Arzneimittelwechselwirkungen und Risiken“ – wie können Daten aus der stationären Krankenversorgung zur Beurteilung beitragen?
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Zusammenfassung Hintergrund Mit der zunehmenden Anzahl eingenommener Arzneimittel steigt die Prävalenz von Medikationsrisiken. Hierzu zählen beispielsweise Arzneimittelwechselwirkungen, welche erwünschte … und unerwünschte Wirkungen einzelner Arzneistoffe reduzieren aber auch verstärken können. Fragestellung Das Verbundvorhaben POLAR (POLypharmazie, Arzneimittelwechselwirkungen und Risiken) hat das Ziel, mit Methoden und Prozessen der Medizininformatikinitiative (MII) auf Basis von „Real World Data“ (stationärer Behandlungsdaten von Universitätskliniken) einen Beitrag zur Detektion von Medikationsrisiken bei Patient:innen mit Polymedikation zu leisten. Im Artikel werden die konkreten klinischen Probleme dargestellt und am konkreten Auswertebeispiel illustriert. Material und Methoden Konkrete pharmakologische Fragestellungen werden algorithmisch abgebildet und an 13 Datenintegrationszentren in verteilten Analysen ausgewertet. Eine wesentliche Voraussetzung für die Anwendung dieser Algorithmen ist die Kerndatensatzstruktur der MII, die auf internationale IT-, Interoperabilitäts- und Terminologiestandards setzt. Ergebnisse In POLAR konnte erstmals gezeigt werden, dass stationäre Behandlungsdaten standortübergreifend auf der Basis abgestimmter, interoperabler Datenaustauschformate datenschutzkonform für Forschungsfragen zu arzneimittelbezogenen Problemen nutzbar gemacht werden können. Schlussfolgerungen Als Zwischenstand in POLAR wird ein erstes vorläufiges Ergebnis einer Analyse gezeigt. Darüber hinaus werden allgemeinere technische, rechtliche, kommunikative Chancen und Herausforderungen dargestellt, wobei der Fokus auf dem Fall der Verwendung stationärer Behandlungsdaten als „Real World Data“ für die Forschung liegt.

Authors: André Scherag, Wahram Andrikyan, Tobias Dreischulte, Pauline Dürr, Martin F Fromm, Jan Gewehr, Ulrich Jaehde, Miriam Kesselmeier, Renke Maas, Petra A Thürmann, Frank Meineke, Daniel Neumann, Julia Palm, Thomas Peschel, Editha Räuscher, Susann Schulze, Torsten Thalheim, Thomas Wendt, Markus Loeffler, D Ammon, W Andrikyan, U Bartz, B Bergh, T Bertsche, O Beyan, S Biergans, H Binder, M Boeker, H Bogatsch, R Böhm, A Böhmer, J Brandes, C Bulin, D Caliskan, I Cascorbi, M Coenen, F Dietz, F Dörje, T Dreischulte, J Drepper, P Dürr, A Dürschmid, F Eckelt, R Eils, A Eisert, C Engel, F Erdfelder, K Farker, M Federbusch, S Franke, N Freier, T Frese, M Fromm, K Fünfgeld, T Ganslandt, J Gewehr, D Grigutsch, W Haefeli, U Hahn, A Härdtlein, R Harnisch, S Härterich, M Hartmann, R Häuslschmid, C Haverkamp, O Heinze, P Horki, M Hug, T Iskra, U Jaehde, S Jäger, P Jürs, C Jüttner, J Kaftan, T Kaiser, K Karsten Dafonte, M Kesselmeier, S Kiefer, S Klasing, O Kohlbacher, D Kraska, S Krause, S Kreutzke, R Krock, K Kuhn, S Lederer, M Lehne, M Löbe, M Loeffler, C Lohr, V Lowitsch, N Lüneburg, M Lüönd, I Lutz, R Maas, U Mansmann, K Marquardt, A Medek, F Meineke, A Merzweiler, A Michel-Backofen, Y Mou, B Mussawy, D Neumann, J Neumann, C Niklas, M Nüchter, K Oswald, J Palm, T Peschel, H Prokosch, J Przybilla, E Räuscher, L Redeker, Y Remane, A Riedel, M Rottenkolber, F Rottmann, F Salman, J Schepers, A Scherag, F Schmidt, S Schmiedl, K Schmitz, G Schneider, A Scholtz, S Schorn, B Schreiweis, S Schulze, A K Schuster, M Schwab, H Seidling, S Semler, K Senft, M Slupina, R Speer, S Stäubert, D Steinbach, C Stelzer, H Stenzhorn, M Strobel, T Thalheim, M Then, P Thürmann, D Tiller, P Tippmann, Y Ucer, S Unger, J Vogel, J Wagner, J Wehrle, D Weichart, L Weisbach, S Welten, T Wendt, R Wettstein, I Wittenberg, R Woltersdorf, M Yahiaoui-Doktor, S Zabka, S Zenker, S Zeynalova, L Zimmermann, D Zöller, für das POLAR-Projekt

Date Published: 1st Sep 2022

Publication Type: Journal article

GRASCCO — The First Publicly Shareable, Multiply-Alienated German Clinical Text Corpus
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

We describe the creation of GRASCCO, a novel German-language corpus composed of some 60 clinical documents with more than.43,000 tokens. GRASCCO is a synthetic corpus resulting from a series of alienation … steps to obfuscate privacy-sensitive information contained in real clinical documents, the true origin of all GRASCCO texts. Therefore, it is publicly shareable without any legal restrictions We also explore whether this corpus still represents common clinical language use by comparison with a real (non-shareable) clinical corpus we developed as a contribution to the Medical Informatics Initiative in Germany (MII) within the SMITH consortium. We find evidence that such a claim can indeed be made.

Editor:

Date Published: 17th Aug 2022

Publication Type: InProceedings

The Leipzig Health Atlas-An Open Platform to Present, Archive, and Share Biomedical Data, Analyses, and Models Online.
LHA - Leipzig Health Atlas

Abstract (Expand)

BACKGROUND: Clinical trials, epidemiological studies, clinical registries, and other prospective research projects, together with patient care services, are main sources of data in the medical research … domain. They serve often as a basis for secondary research in evidence-based medicine, prediction models for disease, and its progression. This data are often neither sufficiently described nor accessible. Related models are often not accessible as a functional program tool for interested users from the health care and biomedical domains. OBJECTIVE: The interdisciplinary project Leipzig Health Atlas (LHA) was developed to close this gap. LHA is an online platform that serves as a sustainable archive providing medical data, metadata, models, and novel phenotypes from clinical trials, epidemiological studies, and other medical research projects. METHODS: Data, models, and phenotypes are described by semantically rich metadata. The platform prefers to share data and models presented in original publications but is also open for nonpublished data. LHA provides and associates unique permanent identifiers for each dataset and model. Hence, the platform can be used to share prepared, quality-assured datasets and models while they are referenced in publications. All managed data, models, and phenotypes in LHA follow the FAIR principles, with public availability or restricted access for specific user groups. RESULTS: The LHA platform is in productive mode (https://www.health-atlas.de/). It is already used by a variety of clinical trial and research groups and is becoming increasingly popular also in the biomedical community. LHA is an integral part of the forthcoming initiative building a national research data infrastructure for health in Germany.

Authors: T. Kirsten, F. A. Meineke, H. Loeffler-Wirth, C. Beger, A. Uciteli, S. Staubert, M. Lobe, R. Hansel, F. G. Rauscher, J. Schuster, T. Peschel, H. Herre, J. Wagner, S. Zachariae, C. Engel, M. Scholz, E. Rahm, H. Binder, M. Loeffler

Date Published: 3rd Aug 2022

Publication Type: Journal article

GRASCCO — the first publicly shareable, multiply-alienated German clinical text corpus
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

We describe the creation of GRASCCO, a novel German-language corpus composed of some 60 clinical documents with more than.43,000 tokens. GRASCCO is a synthetic corpus resulting from a series of alienation … steps to obfuscate privacy-sensitive information contained in real clinical documents, the true origin of all GRASCCO texts. Therefore, it is publicly shareable without any legal restrictions We also explore whether this corpus still represents common clinical language use by comparison with a real (non-shareable) clinical corpus we developed as a contribution to the Medical Informatics Initiative in Germany (MII) within the SMITH consortium. We find evidence that such a claim can indeed be made.

Authors: Luise Modersohn, Stefan Schulz, Christina Lohr, Udo Hahn

Date Published: 1st Aug 2022

Publication Type: InCollection

On the Parametrization of Epidemiologic Models-Lessons from Modelling COVID-19 Epidemic.
Genetical Statistics and Systems Biology, Task Force COVID-19 Leipzig

Abstract (Expand)

Numerous prediction models of SARS-CoV-2 pandemic were proposed in the past. Unknown parameters of these models are often estimated based on observational data. However, lag in case-reporting, changing … testing policy or incompleteness of data lead to biased estimates. Moreover, parametrization is time-dependent due to changing age-structures, emerging virus variants, non-pharmaceutical interventions, and vaccination programs. To cover these aspects, we propose a principled approach to parametrize a SIR-type epidemiologic model by embedding it as a hidden layer into an input-output non-linear dynamical system (IO-NLDS). Observable data are coupled to hidden states of the model by appropriate data models considering possible biases of the data. This includes data issues such as known delays or biases in reporting. We estimate model parameters including their time-dependence by a Bayesian knowledge synthesis process considering parameter ranges derived from external studies as prior information. We applied this approach on a specific SIR-type model and data of Germany and Saxony demonstrating good prediction performances. Our approach can estimate and compare the relative effectiveness of non-pharmaceutical interventions and provide scenarios of the future course of the epidemic under specified conditions. It can be translated to other data sets, i.e., other countries and other SIR-type models.

Authors: Y. Kheifetz, H. Kirsten, M. Scholz

Date Published: 2nd Jul 2022

Publication Type: Journal article

Human Diseases: COVID-19

Data protection-compliant broad consent for secondary use of health care data and human biosamples for (bio)medical research: Towards a new German national standard
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

BACKGROUND: The secondary use of deidentified but not anonymized patient data is a promising approach for enabling precision medicine and learning health care systems. In most national jurisdictions … (e.g., in Europe), this type of secondary use requires patient consent. While various ethical, legal, and technical analyses have stressed the opportunities and challenges for different types of consent over the past decade, no country has yet established a national consent standard accepted by the relevant authorities. METHODS: A working group of the national Medical Informatics Initiative in Germany conducted a requirements analysis and developed a GDPR-compliant broad consent standard. The development included consensus procedures within the Medical Informatics Initiative, a documented consultation process with all relevant stakeholder groups and authorities, and the ultimate submission for approval via the national data protection authorities. RESULTS: This paper presents the broad consent text together with a guidance document on mandatory safeguards for broad consent implementation. The mandatory safeguards comprise i) independent review of individual research projects, ii) organizational measures to protect patients from involuntary disclosure of protected information, and iii) comprehensive information for patients and public transparency. This paper further describes the key issues discussed with the relevant authorities, especially the position on additional or alternative consent approaches such as dynamic consent. DISCUSSION: Both the resulting broad consent text and the national consensus process are relevant for similar activities internationally. A key challenge of aligning consent documents with the various stakeholders was explaining and justifying the decision to use broad consent and the decision against using alternative models such as dynamic consent. Public transparency for all secondary use projects and their results emerged as a key factor in this justification. While currently largely limited to academic medicine in Germany, the first steps for extending this broad consent approach to wider areas of application, including smaller institutions and medical practices, are currently under consideration.

Authors: Sven Zenker, Daniel Strech, Kristina Ihrig, Roland Jahns, Gabriele Müller, Christoph Schickhardt, Georg Schmidt, Ronald Speer, Eva Winkler, Sebastian Graf von Kielmansegg, Johannes Drepper

Date Published: 1st Jul 2022

Publication Type: Journal article

GAN-Based Approaches for Generating Structured Data in the Medical Domain
Synthetica: generation and evaluation of synthetic data

Abstract (Expand)

Modern machine and deep learning methods require large datasets to achieve reliable and robust results. This requirement is often difficult to meet in the medical field, due to data sharing limitations …ng limitations imposed by privacy regulations or the presence of a small number of patients (e.g., rare diseases). To address this data scarcity and to improve the situation, novel generative models such as Generative Adversarial Networks (GANs) have been widely used to generate synthetic data that mimic real data by representing features that reflect health-related information without reference to real patients. In this paper, we consider several GAN models to generate synthetic data used for training binary (malignant/benign) classifiers, and compare their performances in terms of classification accuracy with cases where only real data are considered. We aim to investigate how synthetic data can improve classification accuracy, especially when a small amount of data is available. To this end, we have developed and implemented an evaluation framework where binary classifiers are trained on extended datasets containing both real and synthetic data. The results show improved accuracy for classifiers trained with generated data from more advanced GAN models, even when limited amounts of original data are available.

Authors: Masoud Abedi, Lars Hempel, Sina Sadeghi, Toralf Kirsten

Date Published: 1st Jul 2022

Publication Type: Journal article

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