Publications

966 Publications visible to you, out of a total of 966

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BACKGROUND: The growing interest in the secondary use of electronic health record (EHR) data has increased the number of new data integration and data sharing infrastructures. The present work has been developed in the context of the German Medical Informatics Initiative, where 29 university hospitals agreed to the usage of the Health Level Seven Fast Healthcare Interoperability Resources (FHIR) standard for their newly established data integration centers. This standard is optimized to describe and exchange medical data but less suitable for standard statistical analysis which mostly requires tabular data formats. OBJECTIVES: The objective of this work is to establish a tool that makes FHIR data accessible for standard statistical analysis by providing means to retrieve and transform data from a FHIR server. The tool should be implemented in a programming environment known to most data analysts and offer functions with variable degrees of flexibility and automation catering to users with different levels of FHIR expertise. METHODS: We propose the fhircrackr framework, which allows downloading and flattening FHIR resources for data analysis. The framework supports different download and authentication protocols and gives the user full control over the data that is extracted from the FHIR resources and transformed into tables. We implemented it using the programming language R [1] and published it under the GPL-3 open source license. RESULTS: The framework was successfully applied to both publicly available test data and real-world data from several ongoing studies. While the processing of larger real-world data sets puts a considerable burden on computation time and memory consumption, those challenges can be attenuated with a number of suitable measures like parallelization and temporary storage mechanisms. CONCLUSION: The fhircrackr R package provides an open source solution within an environment that is familiar to most data scientists and helps overcome the practical challenges that still hamper the usage of EHR data for research.

Authors: Julia Palm, Frank A Meineke, Jens Przybilla, Thomas Peschel

Date Published: 2023

Publication Type: Journal article

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BACKGROUND: The Federal Ministry of Education and Research of Germany (BMBF) funds a network of university medicines (NUM) to support COVID-19 and pandemic research at national level. The “COVID-19 Data Exchange Platform” (CODEX) as part of NUM establishes a harmonised infrastructure that supports research use of COVID-19 datasets. The broad consent (BC) of the Medical Informatics Initiative (MII) is agreed by all German federal states and forms the legal base for data processing. All 34 participating university hospitals (NUM sites) work upon a harmonised infrastructural as well as legal basis for their data protection-compliant collection and transfer of their research dataset to the central CODEX platform. Each NUM site ensures that the exchanged consent information conforms to the already-balloted HL7 FHIR consent profiles and the interoperability concept of the MII Task Force “Consent Implementation” (TFCI). The Independent Trusted Third-Party (TTP) of the University Medicine Greifswald supports data protection-compliant data processing and provides the consent management solutions gICS. METHODS: Based on a stakeholder dialogue a required set of FHIR-functionalities was identified and technically specified supported by official FHIR experts. Next, a “TTP-FHIR Gateway” for the HL7 FHIR-compliant exchange of consent information using gICS was implemented. A last step included external integration tests and the development of a pre-configured consent template for the BC for the NUM sites. RESULTS: A FHIR-compliant gICS-release and a corresponding consent template for the BC were provided to all NUM sites in June 2021. All FHIR functionalities comply with the already-balloted FHIR consent profiles of the HL7 Working Group Consent Management. The consent template simplifies the technical BC rollout and the corresponding implementation of the TFCI interoperability concept at the NUM sites. CONCLUSIONS: This article shows that a HL7 FHIR-compliant and interoperable nationwide exchange of consent information could be built using of the consent management software gICS and the provided TTP-FHIR Gateway. The initial functional scope of the solution covers the requirements identified in the NUM-CODEX setting. The semantic correctness of these functionalities was validated by project-partners from the Ludwig-Maximilian University in Munich. The production rollout of the solution package to all NUM sites has started successfully.

Authors: Martin Bialke, Lars Geidel, Christopher Hampf, Arne Blumentritt, Peter Penndorf, Ronny Schuldt, Frank-Michael Moser, Stefan Lang, Patrick Werner, Sebastian Stäubert, Hauke Hund, Fady Albashiti, Jürgen Gührer, Hans-Ulrich Prokosch, Thomas Bahls, Wolfgang Hoffmann

Date Published: 1st Dec 2022

Publication Type: Journal article

Abstract (Expand)

Anti-CD19 CAR-T cell immunotherapy is a hopeful treatment option for patients with B cell lymphomas, however it copes with partly severe adverse effects like neurotoxicity. Single-cell resolved molecular data sets in combination with clinical parametrization allow for comprehensive characterization of cellular subpopulations, their transcriptomic states, and their relation to the adverse effects. We here present a re-analysis of single-cell RNA sequencing data of 24 patients comprising more than 130,000 cells with focus on cellular states and their association to immune cell related neurotoxicity. For this, we developed a single-cell data portraying workflow to disentangle the transcriptional state space with single-cell resolution and its analysis in terms of modularly-composed cellular programs. We demonstrated capabilities of single-cell data portraying to disentangle transcriptional states using intuitive visualization, functional mining, molecular cell stratification, and variability analyses. Our analysis revealed that the T cell composition of the patient's infusion product as well as the spectrum of their transcriptional states of cells derived from patients with low ICANS grade do not markedly differ from those of cells from high ICANS patients, while the relative abundancies, particularly that of cycling cells, of LAG3-mediated exhaustion and of CAR positive cells, vary. Our study provides molecular details of the transcriptomic landscape with possible impact to overcome neurotoxicity.

Authors: H. Loeffler-Wirth, M. Rade, A. Arakelyan, M. Kreuz, M. Loeffler, U. Koehl, K. Reiche, H. Binder

Date Published: 17th Oct 2022

Publication Type: Journal article

Abstract (Expand)

Machine learning (ML) models are developed on a learning dataset covering only a small part of the data of interest. If model predictions are accurate for the learning dataset but fail for unseen data then generalization error is considered high. This problem manifests itself within all major sub-fields of ML but is especially relevant in medical applications. Clinical data structures, patient cohorts, and clinical protocols may be highly biased among hospitals such that sampling of representative learning datasets to learn ML models remains a challenge. As ML models exhibit poor predictive performance over data ranges sparsely or not covered by the learning dataset, in this study, we propose a novel method to assess their generalization capability among different hospitals based on the convex hull (CH) overlap between multivariate datasets. To reduce dimensionality effects, we used a two-step approach. First, CH analysis was applied to find mean CH coverage between each of the two datasets, resulting in an upper bound of the prediction range. Second, 4 types of ML models were trained to classify the origin of a dataset (i.e., from which hospital) and to estimate differences in datasets with respect to underlying distributions. To demonstrate the applicability of our method, we used 4 critical-care patient datasets from different hospitals in Germany and USA. We estimated the similarity of these populations and investigated whether ML models developed on one dataset can be reliably applied to another one. We show that the strongest drop in performance was associated with the poor intersection of convex hulls in the corresponding hospitals’ datasets and with a high performance of ML methods for dataset discrimination. Hence, we suggest the application of our pipeline as a first tool to assess the transferability of trained models. We emphasize that datasets from different hospitals represent heterogeneous data sources, and the transfer from one database to another should be performed with utmost care to avoid implications during real-world applications of the developed models. Further research is needed to develop methods for the adaptation of ML models to new hospitals. In addition, more work should be aimed at the creation of gold-standard datasets that are large and diverse with data from varied application sites.

Authors: Konstantin Sharafutdinov, Jayesh S Bhat, Sebastian Johannes Fritsch, Kateryna Nikulina, Moein E Samadi, Richard Polzin, Hannah Mayer, Gernot Marx, Johannes Bickenbach, Andreas Schuppert

Date Published: 1st Oct 2022

Publication Type: Journal article

Abstract (Expand)

OBJECTIVES: The TMF (Technology, Methods, and Infrastructure for Networked Medical Research) Data Protection Guide (TMF-DP) makes path-breaking recommendations on the subject of data protection in research projects. It includes comprehensive requirements for applications such as patient lists, pseudonymization services, and consent management services. Nevertheless, it lacks a structured, categorized list of requirements for simplified application in research projects and systematic evaluation. The 3LGM2IHE ("Three-layer Graphbased meta model - Integrating the Healthcare Enterprise [IHE] " ) project is funded by the German Research Foundation (DFG). 3LGM2IHE aims to define modeling paradigms and implement modeling tools for planning health care information systems. In addition, one of the goals is to create and publish 3LGM(2) information system architecture design patterns (short "design patterns") for the community as design models in terms of a framework. A structured list of data protection-related requirements based on the TMF-DP is a precondition to integrate functions (3LGM(2) Domain Layer) and building blocks (3LGM(2) Logical Tool Layer) in 3LGM(2) design patterns. METHODS: In order to structure the continuous text of the TMF-DP, requirement types were defined in a first step. In a second step, dependencies and delineations of the definitions were identified. In a third step, the requirements from the TMF-DP were systematically extracted. Based on the identified lists of requirements, a fourth step included the comparison of the identified requirements with exemplary open source tools as provided by the "Independent Trusted Third Party of the University Medicine Greifswald" (TTP tools). RESULTS: As a result, four lists of requirements were created, which contain requirements for the "patient list", the "pseudonymization service", and the "consent management", as well as cross-component requirements from the TMF-DP chapter 6 in a structured form. Further to requirements (1), possible variants (2) of implementations (to fulfill a single requirement) and recommendations (3) were identified. A comparison of the requirements lists with the functional scopes of the open source tools E-PIX (record linkage), gPAS (pseudonym management), and gICS (consent management) has shown that these fulfill more than 80% of the requirements. CONCLUSIONS: A structured set of data protection-related requirements facilitates a systematic evaluation of implementations with respect to the fulfillment of the TMF-DP guidelines. These re-usable lists provide a decision aid for the selection of suitable tools for new research projects. As a result, these lists form the basis for the development of data protection-related 3LGM(2) design patterns as part of the 3LGM2IHE project.

Authors: R. Gott, S. Staubert, A. Strubing, A. Winter, A. Merzweiler, B. Bergh, K. Kaulke, T. Bahls, W. Hoffmann, M. Bialke

Date Published: 24th Sep 2022

Publication Type: Journal article

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

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

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