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

被引:9
|
作者
Hosch, Rene [1 ,2 ]
Baldini, Giulia [1 ,2 ]
Parmar, Vicky [1 ,2 ]
Borys, Katarzyna [1 ,2 ]
Koitka, Sven [1 ,2 ]
Engelke, Merlin [1 ,2 ]
Arzideh, Kamyar [2 ,3 ]
Ulrich, Moritz [2 ,3 ]
Nensa, Felix [1 ,2 ]
机构
[1] Univ Hosp Essen, Inst Intervent & Diagnost Radiol & Neuroradiol, Hufelandstr 55, D-45147 Essen, Germany
[2] Univ Hosp Essen, Inst Artificial Intelligence Med, Girardetstr 2, D-45131 Essen, Germany
[3] Univ Hosp Essen, Data Integrat Ctr, Cent IT Dept, Hufelandstr 55, D-45147 Essen, Germany
关键词
Electronic patient record; FHIR; !text type='Python']Python[!/text; Dataframe; Information extraction; Dicom; HEALTH;
D O I
10.1186/s12913-023-09498-1
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
收藏
页数:15
相关论文
共 50 条
  • [41] Applications of Python']Python to evaluate environmental data science problems
    Kadiyala, Akhil
    Kumar, Ashok
    ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY, 2017, 36 (06) : 1580 - 1586
  • [42] Nmrglue: an open source Python']Python package for the analysis of multidimensional NMR data
    Helmus, Jonathan J.
    Jaroniec, Christopher P.
    JOURNAL OF BIOMOLECULAR NMR, 2013, 55 (04) : 355 - 367
  • [43] pyBioPortal: a Python']Python package for simplifying cBioPortal data access in cancer research
    Valerio, Matteo
    Inno, Alessandro
    Gori, Stefania
    JAMIA OPEN, 2024, 8 (01)
  • [44] MVTS-Data Toolkit: A Python']Python package for preprocessing multivariate time series data
    Ahmadzadeh, Azim
    Sinha, Kankana
    Aydin, Berkay
    Angryk, Rafal A.
    SOFTWAREX, 2020, 12
  • [45] LDAQ: An Open-Source Python']Python Package for Data Acquisition and Signal Generation
    Kosir, Tilen
    Zaletelj, Klemen
    Slavic, Janko
    SPECIAL TOPICS IN STRUCTURAL DYNAMICS & EXPERIMENTAL TECHNIQUES, VOL 5, 2024, : 109 - 111
  • [46] NiftyPAD-Novel Python']Python Package for Quantitative Analysis of Dynamic PET Data
    Jiao, Jieqing
    Heeman, Fiona
    Dixon, Rachael
    Wimberley, Catriona
    Alves, Isadora Lopes
    Gispert, Juan Domingo
    Lammertsma, Adriaan A.
    van Berckel, Bart N. M.
    da Costa-Luis, Casper
    Markiewicz, Pawel
    Cash, David M.
    Cardoso, M. Jorge
    Ourselin, Sebastien
    Yaqub, Maqsood
    Barkhof, Frederik
    NEUROINFORMATICS, 2023, 21 (02) : 457 - 468
  • [47] PyRINEX: a new multi-purpose Python']Python package for GNSS RINEX data
    Han, Jinzhen
    Lee, Seung Jun
    Yun, Hong Sik
    Kim, Kwang Bae
    Bae, Sang Won
    PEERJ COMPUTER SCIENCE, 2024, 10 : 1 - 19
  • [48] spectrum_utils: A Python']Python Package for Mass Spectrometry Data Processing and Visualization
    Bittremieux, Wout
    ANALYTICAL CHEMISTRY, 2020, 92 (01) : 659 - 661
  • [49] pyActigraphy: Open-source python']python package for actigraphy data visualization and analysis
    Hammad, Gregory
    Reyt, Mathilde
    Beliy, Nikita
    Baillet, Marion
    Deantoni, Michele
    Lesoinne, Alexia
    Muto, Vincenzo
    Schmidt, Christina
    PLOS COMPUTATIONAL BIOLOGY, 2021, 17 (10)
  • [50] Data Science in Stata 16: Frames, Lasso, and Python']Python Integration
    Ho, Anson T. Y.
    Huynh, Kim P.
    Jacho-Chavez, David T.
    Rojas-Baez, Diego
    JOURNAL OF STATISTICAL SOFTWARE, 2021, 98 (SR1): : 1 - 9