Cargando…

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

BACKGROUND: We 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...

Descripción completa

Detalles Bibliográficos
Autores principales: Hosch, René, Baldini, Giulia, Parmar, Vicky, Borys, Katarzyna, Koitka, Sven, Engelke, Merlin, Arzideh, Kamyar, Ulrich, Moritz, Nensa, Felix
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10326955/
https://www.ncbi.nlm.nih.gov/pubmed/37415138
http://dx.doi.org/10.1186/s12913-023-09498-1
_version_ 1785069533086613504
author Hosch, René
Baldini, Giulia
Parmar, Vicky
Borys, Katarzyna
Koitka, Sven
Engelke, Merlin
Arzideh, Kamyar
Ulrich, Moritz
Nensa, Felix
author_facet Hosch, René
Baldini, Giulia
Parmar, Vicky
Borys, Katarzyna
Koitka, Sven
Engelke, Merlin
Arzideh, Kamyar
Ulrich, Moritz
Nensa, Felix
author_sort Hosch, René
collection PubMed
description BACKGROUND: We 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. METHODS: The 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. RESULTS: As 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. CONCLUSIONS: FHIR-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.
format Online
Article
Text
id pubmed-10326955
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-103269552023-07-08 FHIR-PYrate: a data science friendly Python package to query FHIR servers Hosch, René Baldini, Giulia Parmar, Vicky Borys, Katarzyna Koitka, Sven Engelke, Merlin Arzideh, Kamyar Ulrich, Moritz Nensa, Felix BMC Health Serv Res Software BACKGROUND: We 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. METHODS: The 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. RESULTS: As 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. CONCLUSIONS: FHIR-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. BioMed Central 2023-07-06 /pmc/articles/PMC10326955/ /pubmed/37415138 http://dx.doi.org/10.1186/s12913-023-09498-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Hosch, René
Baldini, Giulia
Parmar, Vicky
Borys, Katarzyna
Koitka, Sven
Engelke, Merlin
Arzideh, Kamyar
Ulrich, Moritz
Nensa, Felix
FHIR-PYrate: a data science friendly Python package to query FHIR servers
title FHIR-PYrate: a data science friendly Python package to query FHIR servers
title_full FHIR-PYrate: a data science friendly Python package to query FHIR servers
title_fullStr FHIR-PYrate: a data science friendly Python package to query FHIR servers
title_full_unstemmed FHIR-PYrate: a data science friendly Python package to query FHIR servers
title_short FHIR-PYrate: a data science friendly Python package to query FHIR servers
title_sort fhir-pyrate: a data science friendly python package to query fhir servers
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10326955/
https://www.ncbi.nlm.nih.gov/pubmed/37415138
http://dx.doi.org/10.1186/s12913-023-09498-1
work_keys_str_mv AT hoschrene fhirpyrateadatasciencefriendlypythonpackagetoqueryfhirservers
AT baldinigiulia fhirpyrateadatasciencefriendlypythonpackagetoqueryfhirservers
AT parmarvicky fhirpyrateadatasciencefriendlypythonpackagetoqueryfhirservers
AT boryskatarzyna fhirpyrateadatasciencefriendlypythonpackagetoqueryfhirservers
AT koitkasven fhirpyrateadatasciencefriendlypythonpackagetoqueryfhirservers
AT engelkemerlin fhirpyrateadatasciencefriendlypythonpackagetoqueryfhirservers
AT arzidehkamyar fhirpyrateadatasciencefriendlypythonpackagetoqueryfhirservers
AT ulrichmoritz fhirpyrateadatasciencefriendlypythonpackagetoqueryfhirservers
AT nensafelix fhirpyrateadatasciencefriendlypythonpackagetoqueryfhirservers