ArticleBMC health services research2023
FHIR-PYrate: a data science friendly Python package to query FHIR servers.
Article in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Topogram-based anatomical labelling of CT series: anatomy-aware CT data processing using deep learning.European radiology · 2026Article
- Benchmarking Fast Healthcare Interoperability Resources-Based Analytics: Quantitative Study of RESTful Server Queries and Big Data Engines.JMIR medical informatics · 2026Article
- [Evidence generation and methodological consultation by the project "EVAluation research based on data from routine clinical care 4 the Medical Informatics Initiative" (EVA4MII)].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2026Review
- Fundamentals of big data and artificial intelligence in transfusion medicine.Vox sanguinis · 2026Review
- SQL on FHIR - Tabular views of FHIR data using FHIRPath.NPJ digital medicine · 2025Article
- Fully volumetric body composition analysis for prognostic overall survival stratification in melanoma patients.Journal of translational medicine · 2025Article
- A Transformer-Based Pipeline for German Clinical Document De-Identification.Applied clinical informatics · 2025Article
- Article
- Use of Real-World FHIR Data Combined with Context-Sensitive Decision Modeling to Guide Sentinel Biopsy in Melanoma.Journal of clinical medicine · 2024Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundWe present FHIR-PYrate, a Python package to handle the full clinical data collection and extraction process. The software is to be plugged into a modern hospital domain, where electronic patient records are used to handle the entire patient's history. Most research institutes follow the same procedures to build study cohorts, but mainly in a non-standardized and repetitive way. As a result, researchers spend time writing boilerplate code, which could be used for more challenging tasks.
methodsThe package can improve and simplify existing processes in the clinical research environment. It collects all needed functionalities into a straightforward interface that can be used to query a FHIR server, download imaging studies and filter clinical documents. The full capacity of the search mechanism of the FHIR REST API is available to the user, leading to a uniform querying process for all resources, thus simplifying the customization of each use case. Additionally, valuable features like parallelization and filtering are included to make it more performant.
resultsAs an exemplary practical application, the package can be used to analyze the prognostic significance of routine CT imaging and clinical data in breast cancer with tumor metastases in the lungs. In this example, the initial patient cohort is first collected using ICD-10 codes. For these patients, the survival information is also gathered. Some additional clinical data is retrieved, and CT scans of the thorax are downloaded. Finally, the survival analysis can be computed using a deep learning model with the CT scans, the TNM staging and positivity of relevant markers as input. This process may vary depending on the FHIR server and available clinical data, and can be customized to cover even more use cases.
conclusionsFHIR-PYrate opens up the possibility to quickly and easily retrieve FHIR data, download image data, and search medical documents for keywords within a Python package. With the demonstrated functionality, FHIR-PYrate opens an easy way to assemble research collectives automatically.
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