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Synthetic data for privacy-preserving clinical risk prediction.

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  • معلومة اضافية
    • المصدر:
      Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
    • بيانات النشر:
      Original Publication: London : Nature Publishing Group, copyright 2011-
    • الموضوع:
      Privacy*; Humans ; Lung Neoplasms ; Prognosis ; Biological Specimen Banks ; Information Dissemination ; United Kingdom ; Female ; Male
    • نبذة مختصرة :
      Synthetic data promise privacy-preserving data sharing for healthcare research and development. Compared with other privacy-enhancing approaches-such as federated learning-analyses performed on synthetic data can be applied downstream without modification, such that synthetic data can act in place of real data for a wide range of use cases. However, the role that synthetic data might play in all aspects of clinical model development remains unknown. In this work, we used state-of-the-art generators explicitly designed for privacy preservation to create a synthetic version of ever-smokers in the UK Biobank before building prognostic models for lung cancer under several data release assumptions. We demonstrate that synthetic data can be effectively used throughout the medical prognostic modeling pipeline even without eventual access to the real data. Furthermore, we show the implications of different data release approaches on how synthetic biobank data could be deployed within the healthcare system.
      (© 2024. The Author(s).)
    • References:
      IEEE J Biomed Health Inform. 2020 Aug;24(8):2378-2388. (PMID: 32167919)
      NPJ Digit Med. 2020 Nov 9;3(1):147. (PMID: 33299100)
      Stat Med. 2011 May 10;30(10):1105-17. (PMID: 21484848)
      Ann Intern Med. 2019 Mar 5;170(5):332-334. (PMID: 30776795)
      PLoS One. 2011;6(12):e28071. (PMID: 22164229)
      Nat Methods. 2016 Mar;13(3):251-6. (PMID: 26828419)
      BMC Med Res Methodol. 2020 May 7;20(1):108. (PMID: 32381039)
      Stat Med. 1999 Sep 15-30;18(17-18):2529-45. (PMID: 10474158)
      BMJ Med. 2022 Sep 26;1(1):e000167. (PMID: 36936569)
      PLoS Med. 2015 Mar 31;12(3):e1001779. (PMID: 25826379)
      BMC Med Res Methodol. 2018 Feb 26;18(1):24. (PMID: 29482517)
      Nat Rev Mol Cell Biol. 2022 Jan;23(1):40-55. (PMID: 34518686)
      Neural Comput. 1999 Feb 15;11(2):443-82. (PMID: 9950739)
      IEEE Trans Pattern Anal Mach Intell. 2022 Nov;44(11):7327-7347. (PMID: 34591756)
      Lancet. 2019 Nov 23;394(10212):1902. (PMID: 31708190)
      Lung Cancer. 2020 Sep;147:154-186. (PMID: 32721652)
      Health Aff (Millwood). 2018 May;37(5):702-709. (PMID: 29733719)
    • Grant Information:
      EICEDAAP\100012 United Kingdom CRUK_ Cancer Research UK
    • Contributed Indexing:
      Keywords: Machine learning; Risk-prediction; Synthetic data
    • الموضوع:
      Date Created: 20241028 Date Completed: 20241028 Latest Revision: 20241030
    • الموضوع:
      20241031
    • الرقم المعرف:
      PMC11514179
    • الرقم المعرف:
      10.1038/s41598-024-72894-y
    • الرقم المعرف:
      39463411