FHIR-PYrate: a data science friendly Python package to query FHIR servers.
Dataframe
Dicom
Electronic patient record
FHIR
Information extraction
Python
Journal
BMC health services research
ISSN: 1472-6963
Titre abrégé: BMC Health Serv Res
Pays: England
ID NLM: 101088677
Informations de publication
Date de publication:
06 Jul 2023
06 Jul 2023
Historique:
received:
23
09
2022
accepted:
03
05
2023
medline:
10
7
2023
pubmed:
7
7
2023
entrez:
6
7
2023
Statut:
epublish
Résumé
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. 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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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
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.
Identifiants
pubmed: 37415138
doi: 10.1186/s12913-023-09498-1
pii: 10.1186/s12913-023-09498-1
pmc: PMC10326955
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
734Informations de copyright
© 2023. The Author(s).
Références
Appl Clin Inform. 2023 Jan;14(1):54-64
pubmed: 36696915
NPJ Genom Med. 2020 Mar 18;5:13
pubmed: 32194985
BMC Med Inform Decis Mak. 2020 Mar 11;20(1):53
pubmed: 32160884
NPJ Digit Med. 2020 Oct 16;3:136
pubmed: 33083571
J Biomed Inform. 2020 Sep;109:103517
pubmed: 32712157
Cancers (Basel). 2021 Dec 08;13(24):
pubmed: 34944806
Radiology. 2021 Jul;300(1):120-129
pubmed: 33944629
Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:4687-4690
pubmed: 36085809
Eur Radiol. 2021 Apr;31(4):1795-1804
pubmed: 32945971
J Am Med Inform Assoc. 2022 Aug 16;29(9):1642-1653
pubmed: 35818340
JMIR Med Inform. 2021 Apr 1;9(4):e25645
pubmed: 33792554
J Am Med Inform Assoc. 2001 Nov-Dec;8(6):552-69
pubmed: 11687563
Cancers (Basel). 2020 Mar 05;12(3):
pubmed: 32150991
BMC Med Inform Decis Mak. 2019 Aug 22;19(1):168
pubmed: 31438960
J Pathol Inform. 2018 Nov 02;9:37
pubmed: 30533276
Stud Health Technol Inform. 2021 May 27;281:1112-1113
pubmed: 34042862
JMIR Med Inform. 2017 Jan 05;5(1):e1
pubmed: 28057607
J Am Med Inform Assoc. 2016 Sep;23(5):899-908
pubmed: 26911829
J Digit Imaging. 2018 Jun;31(3):321-326
pubmed: 29748852
Breast Cancer Res. 2020 Jun 9;22(1):61
pubmed: 32517735
Trials. 2021 Aug 16;22(1):537
pubmed: 34399832
P T. 2017 Sep;42(9):572-575
pubmed: 28890644
J Med Syst. 2018 Sep 29;42(11):214
pubmed: 30269237
Stud Health Technol Inform. 2022 Feb 1;288:85-99
pubmed: 35102831
NPJ Digit Med. 2018 May 8;1:18
pubmed: 31304302
J Biomed Inform. 2019 Jun;94:103176
pubmed: 30980962
Stud Health Technol Inform. 2019 Sep 3;267:52-58
pubmed: 31483254
J Am Med Inform Assoc. 2017 Nov 01;24(6):1142-1148
pubmed: 29016973
Sci Rep. 2022 Oct 1;12(1):16479
pubmed: 36183002