This paper is in the following e-collection/theme issue:

Standards and Interoperability (60) Big Data (215) Machine Learning (1370) Decision Support for Health Professionals (1127) Electronic Health Records (961) Artificial Intelligence (1186)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43847, first published .
A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study

A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study

A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study

Authors of this article:

Elena Williams1 Author Orcid Image ;   Manuel Kienast1 Author Orcid Image ;   Evelyn Medawar1 Author Orcid Image ;   Janis Reinelt1 Author Orcid Image ;   Alberto Merola1 Author Orcid Image ;   Sophie Anne Ines Klopfenstein2 Author Orcid Image ;   Anne Rike Flint2 Author Orcid Image ;   Patrick Heeren2 Author Orcid Image ;   Akira-Sebastian Poncette2 Author Orcid Image ;   Felix Balzer2 Author Orcid Image ;   Julian Beimes3 Author Orcid Image ;   Paul von Bünau3 Author Orcid Image ;   Jonas Chromik4 Author Orcid Image ;   Bert Arnrich4 Author Orcid Image ;   Nico Scherf5 Author Orcid Image ;   Sebastian Niehaus1 Author Orcid Image
Article Authors Cited by (4) Tweetations (2) Metrics

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  2. Gruson D. Emerging Technologies in Healthcare and Laboratory Medicine: Trends and Need for a Roadmap to Sustainable Implementation. Balkan Medical Journal 2024:85 View
  3. Chen J, Stumpe M, Cohen E. Evolving From Discrete Molecular Data Integrations to Actionable Molecular Insights Within the Electronic Health Record. JCO Clinical Cancer Informatics 2024;(8) View
  4. Han Y, Li Z, Wei T, Zuo X, Liu M, Ma B, Geng Z. Production capacity prediction based response conditions optimization of straw reforming using attention-enhanced convolutional LSTM integrating data expansion. Applied Energy 2024;365:123253 View