A5078462201- Scientific Computing and Data Management
- Artificial Intelligence in Healthcare
- Radiomics and Machine Learning in Medical Imaging
- Electronic Health Records Systems
- Research Data Management Practices
- Ethics in Clinical Research
- Clinical practice guidelines implementation
- Artificial Intelligence in Healthcare and Education
- Chemical Reactions and Isotopes
- Biomedical Text Mining and Ontologies
- Data Quality and Management
- Machine Learning in Healthcare
- Privacy-Preserving Technologies in Data
Universitätsklinikum Erlangen
2023-2025
Friedrich-Alexander-Universität Erlangen-Nürnberg
2019-2023
To take full advantage of decision support, machine learning, and patient-level prediction models, it is important that models are not only created, but also deployed in a clinical setting. The KETOS platform demonstrated this work implements tool for researchers allowing them to perform statistical analyses deploy resulting secure environment.The proposed system uses Docker virtualization provide with reproducible data analysis development environments, accessible via Jupyter Notebook,...
Background: The accumulation of Real-World Data (RWD) from Electronic Health Records (EHRs) and registries offers substantial potential for generating Evidence (RWE). However, the ability to generate robust evidence real-world data hinges on its quality. This is especially critical when heterogeneous first transformed into standardized, research-ready models. Objective: study presents an approach assessing completeness through a pipeline extracting transforming oncological RWD. Methods: We...
Abstract Background The increasing availability of molecular and clinical data cancer patients combined with novel machine learning techniques has the potential to enhance decision support, example, for assessing a patient's relapse risk. While these prediction models often produce promising results, deployment in settings is rarely pursued. Objectives In this study, we demonstrate how tools can be integrated generically into setting provide an exemplary use case predicting risk melanoma...
In the last decade numerous real-world data networks have been established in order to leverage value of from electronic health records for medical research. Germany, a nation-wide network based on record all German university hospitals has within Medical Informatics Initiative (MII) and recently opened researcherst' access through Portal Research Data (FDPG). Bavaria, six joined forces Bavarian Cancer Center (BZKF). The oncology departments aim at establishing federated observational...
<sec> <title>BACKGROUND</title> Real-world data (RWD) from sources like administrative claims, electronic health records, and cancer registries offer insights into patient populations beyond the tightly regulated environment of randomized controlled trials. To leverage this to advance research, six university hospitals in Bavaria have established a joint research IT infrastructure. </sec> <title>OBJECTIVE</title> This article aims outline design, implementation, deployment modular...
Real-world data (RWD) from sources like administrative claims, electronic health records, and cancer registries offer insights into patient populations beyond the tightly regulated environment of randomized controlled trials. To leverage this to advance research, 6 university hospitals in Bavaria have established a joint research IT infrastructure. This study aimed outline design, implementation, deployment modular transformation pipeline that transforms oncological RWD Health Level 7 (HL7)...