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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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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 |
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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 |
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