FHIR-PYrate: a data science friendly Python package to query FHIR servers
Abstract 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 fol...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-07-01
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| Series: | BMC Health Services Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12913-023-09498-1 |
| _version_ | 1827905638634618880 |
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| author | René Hosch Giulia Baldini Vicky Parmar Katarzyna Borys Sven Koitka Merlin Engelke Kamyar Arzideh Moritz Ulrich Felix Nensa |
| author_facet | René Hosch Giulia Baldini Vicky Parmar Katarzyna Borys Sven Koitka Merlin Engelke Kamyar Arzideh Moritz Ulrich Felix Nensa |
| author_sort | René Hosch |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-13T00:44:07Z |
| format | Article |
| id | doaj.art-f92487f5ad3c405d82a578620982a61e |
| institution | Directory Open Access Journal |
| issn | 1472-6963 |
| language | English |
| last_indexed | 2024-03-13T00:44:07Z |
| publishDate | 2023-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Health Services Research |
| spelling | doaj.art-f92487f5ad3c405d82a578620982a61e2023-07-09T11:09:43ZengBMCBMC Health Services Research1472-69632023-07-0123111510.1186/s12913-023-09498-1FHIR-PYrate: a data science friendly Python package to query FHIR serversRené Hosch0Giulia Baldini1Vicky Parmar2Katarzyna Borys3Sven Koitka4Merlin Engelke5Kamyar Arzideh6Moritz Ulrich7Felix Nensa8Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital EssenInstitute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital EssenInstitute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital EssenInstitute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital EssenInstitute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital EssenInstitute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital EssenInstitute for Artificial Intelligence in Medicine, University Hospital EssenInstitute for Artificial Intelligence in Medicine, University Hospital EssenInstitute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital EssenAbstract 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.https://doi.org/10.1186/s12913-023-09498-1Electronic patient recordFHIRPythonDataframeInformation extractionDicom |
| spellingShingle | René Hosch Giulia Baldini Vicky Parmar Katarzyna Borys Sven Koitka Merlin Engelke Kamyar Arzideh Moritz Ulrich Felix Nensa FHIR-PYrate: a data science friendly Python package to query FHIR servers BMC Health Services Research Electronic patient record FHIR Python Dataframe Information extraction Dicom |
| 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 | Electronic patient record FHIR Python Dataframe Information extraction Dicom |
| url | https://doi.org/10.1186/s12913-023-09498-1 |
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