Jens Przybilla

About Jens Przybilla:
No description specified

LHA ID: 83EPJA0MP7-7

Location:  Germany

ORCID: https://orcid.org/0000-0002-3662-1023

Joined: 13th Aug 2020

Expertise: bioinformatics, medical informatics, epigenetics, Single Cell modelling, Theoretical Physics

Tools: Mathematica, Linux, Agent-based modelling

project_administrator
help Their tags

This person has not yet tagged anything.

Related items

POLAR - Polypharmacy, Drug Interactions, Risks

The aim of the project is to use methods and processes of the Medical Informatics Initiative (MII) to contribute to the detection of health risks in patients with polypharmacy. Polypharmacy occurs especially in elderly patients with multimorbidity. Polypharmacy associated with an increased risk for medication errors and drug-drug or drug-disease interactions, which either reduce or intensify the desired effect of individual active substances or lead to undesired adverse drug effects (ADE). In the ...

Programme: MII - Medical Informatics Initiative

Public web page: https://www.medizininformatik-initiative.de/en/POLAR

CapSys - Systems Medicine of Community Acquired Pneumonia: inactive since

CAPSyS is an interdisciplinary research network that investigates the causes and course of severe pneumonia. The network works in five subprojects at seven locations throughout Germany. CAPSyS is funded by the Federal Ministry of Education and Research (BMBF) with 3.8 million euros.

Programme: This Project is not associated with a Programme

Public web page: https://www.capsys.imise.uni-leipzig.de

Start date: 1st Jan 2014

End date: 1st Jan 2019

CAPSys - Footprints of Sepsis Framed Within Community Acquired Pneumonia in the Blood Transcriptome
No description specified

Programme: This Project is not associated with a Programme

Public web page: Not specified

Start date: 1st Jan 2017

e:Med: inactive since

e:Med has the objective of establishing systems medicine in Germany. e:Med promotes system-oriented research into diseases in order to facilitate improved prevention, more comprehensive diagnostics and individually adjusted therapy schemes in individualized medicine. The program brings together scientists with molecular-genetic, clinical, mathematical and information technology expertise, with the objective of ensuring that research results quickly benefit patients.

Programme: Default Programme

Public web page: https://www.sys-med.de/en/

"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

“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

Markov State Modelling of Disease Courses and Mortality Risks of Patients with Community-Acquired Pneumonia
Genetical Statistics and Systems Biology

Abstract (Expand)

Community-acquired pneumonia (CAP) is one of the most frequent infectious diseases worldwide, with high lethality. Risk evaluation is well established at hospital admission, and re-evaluation is advised … for patients at higher risk. However, severe disease courses may develop from all levels of severity. We propose a stochastic continuous-time Markov model describing daily development of time courses of CAP severity. Disease states were defined based on the Sequential Organ Failure Assessment (SOFA) score. Model calibration was based on longitudinal data from 2838 patients with a primary diagnosis of CAP from four clinical studies (PROGRESS, MAXSEP, SISPCT, VISEP). We categorized CAP severity into five disease states and estimated transition probabilities for CAP progression between these states and corresponding sojourn times. Good agreement between model predictions and clinical data was observed. Time courses of mortality were correctly predicted for up to 28 days, including validation with patient data not used for model calibration. We conclude that CAP disease course follows a Markov process, suggesting the necessity of daily monitoring and re-evaluation of patient’s risk. Our model can be used for regular updates of risk assessments of patients and could improve the design of clinical trials by estimating transition rates for different risk groups.

Authors: Jens Przybilla, Peter Ahnert, Holger Bogatsch, Frank Bloos, Frank M. Brunkhorst, Critical Care Trials Group SepNet, Study Group Progress, Michael Bauer, Markus Loeffler, Martin Witzenrath, Norbert Suttorp, Markus Scholz

Date Published: 1st Feb 2020

Publication Type: Journal article

MII - Medical Informatics Initiative

The medical informatics initiative was created to close the gap between research and healthcare. All of Germany’s university hospitals have joined forces with research institutions, businesses, health insurers, and patient advocacy groups to create a framework that harnesses research findings to the direct benefit of patients. The German Federal Ministry of Education and Research (BMBF) is investing around 160 million euros in the programme through 2021. The digitisation of medicine is creating ...

Projects: Methodical Use Case PheP, SMITH - Smart Medical Information Technology for Healthcare, POLAR - Polypharmacy, Drug Interactions, Risks

Web page: https://www.medizininformatik-initiative.de/en

Navigate

Health Atlas - Local Data Hub/Leipzig

The Health Atlas - Local Data Hub/Leipzig is an alliance of medical ontologists, medical systems biologists and clinical trials groups to design and implement a multi-functional and quality-assured atlas. It provides models, data and metadata on specific use cases from medical research fields.

Registered Repository

re3data.org Badge
Powered by
 (v.1.13.0-master)
Health Atlas | Funding and Programmes | Credits | Terms & Conditions | Privacy Policy | Imprint
Copyright © 2008 - 2021 The University of Manchester and HITS gGmbH
Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig

By continuing to use this site you agree to the use of cookies