Evidence map›Paper›PMID 37415138›Full record

ArticleBMC health services research2023

FHIR-PYrate: a data science friendly Python package to query FHIR servers.

René Hosch, Giulia Baldini, Vicky Parmar, Katarzyna Borys, Sven Koitka, Merlin Engelke, Kamyar Arzideh, Moritz Ulrich, Felix Nensa

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

René Hosch *Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany.ORCID https://orcid.org/0000-0003-1760-2342
Giulia Baldini *Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany. giulia.baldini@uk-essen.de.ORCID http://orcid.org/0000-0002-5929-0271
Vicky ParmarInstitute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany.ORCID https://orcid.org/0000-0002-0383-0435
Katarzyna BorysInstitute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany.ORCID https://orcid.org/0000-0001-6987-6041
Sven KoitkaInstitute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany.ORCID https://orcid.org/0000-0001-9704-1180
Merlin EngelkeInstitute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany.ORCID https://orcid.org/0000-0002-8463-8786
Kamyar ArzidehInstitute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, Essen, 45131, Germany.ORCID https://orcid.org/0009-0005-6074-804X
Moritz UlrichInstitute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, Essen, 45131, Germany.
Felix NensaInstitute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany.ORCID https://orcid.org/0000-0002-5811-7100

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Data ScienceHealth Level SevenElectronic Health RecordsHumansSoftwareTomography, X-Ray ComputedDataframeDicomElectronic patient recordFHIRInformation extractionPython

Identifiers

PMID37415138
PMCPMC10326955

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.