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1004 Publications visible to you, out of a total of 1004
"fhircrackr": An R Package Unlocking Fast Healthcare Interoperability Resources for Statistical Analysis.
POLAR - Polypharmacy, Drug Interactions, Risks

Abstract (Expand)

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: J. Palm, F. A. Meineke, J. Przybilla, T. Peschel

Date Published: 25th Jan 2023

Publication Type: Journal article

Analysis of chest X-ray for COVID-19 diagnosis as a use case for an HPC-enabled data analysis and machine learning platform for medical diagnosis support
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

The COVID-19 pandemic shed light on the need for quick diagnosis tools in healthcare, leading to the development of several algorithmic models for disease detection. Though these models are relatively … easy to build, their training requires a lot of data, storage, and resources, which may not be available for use by medical institutions or could be beyond the skillset of the people who most need these tools. This paper describes a data analysis and machine learning platform that takes advantage of high-performance computing infrastructure for medical diagnosis support applications. This platform is validated by re-training a previously published deep learning model (COVID-Net) on new data, where it is shown that the performance of the model is improved through large-scale hyperparameter optimisation that uncovered optimal training parameter combinations. The per-class accuracy of the model, especially for COVID-19 and pneumonia, is higher when using the tuned hyperparameters (healthy: 96.5%; pneumonia: 61.5%; COVID-19: 78.9%) as opposed to parameters chosen through traditional methods (healthy: 93.6%; pneumonia: 46.1%; COVID-19: 76.3%). Furthermore, training speed-up analysis shows a major decrease in training time as resources increase, from 207 min using 1 node to 54 min when distributed over 32 nodes, but highlights the presence of a cut-off point where the communication overhead begins to affect performance. The developed platform is intended to provide the medical field with a technical environment for developing novel portable artificial-intelligence-based tools for diagnosis support.

Authors: Chadi Barakat, Marcel Aach, Andreas Schuppert, Sigur\dhur Brynjólfsson, Sebastian Fritsch, Morris Riedel

Date Published: 2023

Publication Type: Journal article

“fhircrackr”: An R package unlocking fast healthcare interoperability resources for statistical analysis
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

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

Health Information Systems: Technological and Management Perspectives
Semantisches Netz des integrierten Informationsmanagements im Krankenhaus (SNIK)

Abstract

Not specified

Authors: Alfred Winter, Elske Ammenwerth, Reinhold Haux, Michael Marschollek, Bianca Steiner, Franziska Jahn

Date Published: 2023

Publication Type: Book

Question Answering auf einer Ontologie des Informationsmanagements im Krankenhaus
Semantisches Netz des integrierten Informationsmanagements im Krankenhaus (SNIK)

Abstract

Not specified

Author: Hannes Raphael Brunsch

Date Published: 2023

Publication Type: InProceedings

A Linked Open Data-Based Terminology to Describe Libre/Free and Open-source Software: Incremental Development Study
Health IT Ontology (HITO)

Abstract

Not specified

Authors: Franziska Jahn, Elske Ammenwerth, Verena Dornauer, Konrad Høffner, Michelle Bindel, Thomas Karopka, Alfred Winter

Date Published: 2023

Publication Type: Journal article

A FHIR has been lit on gICS: facilitating the standardised exchange of informed consent in a large network of university medicine
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

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

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