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

Viewpoints on and Experiences with Digital Technologies in Health (90) Secondary Use of Clinical Data for Research and Surveillance (432) Electronic Health Records (1212) Machine Learning (1816) Clinical Informatics (1190)

Published on in Vol 10, No 10 (2022): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38557, first published .
Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities

Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities

Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities

Authors of this article:

Alexander Maletzky1 Author Orcid Image ;   Carl Böck2 Author Orcid Image ;   Thomas Tschoellitsch3 Author Orcid Image ;   Theresa Roland4 Author Orcid Image ;   Helga Ludwig4 Author Orcid Image ;   Stefan Thumfart1 Author Orcid Image ;   Michael Giretzlehner1 Author Orcid Image ;   Sepp Hochreiter4 Author Orcid Image ;   Jens Meier3 Author Orcid Image
Article Authors Cited by (11) Tweetations (3) Metrics

Journals

  1. Joyce C, Markossian T, Nikolaides J, Ramsey E, Thompson H, Rojas J, Sharma B, Dligach D, Oguss M, Cooper R, Afshar M. The Evaluation of a Clinical Decision Support Tool Using Natural Language Processing to Screen Hospitalized Adults for Unhealthy Substance Use: Protocol for a Quasi-Experimental Design. JMIR Research Protocols 2022;11(12):e42971 View
  2. Johns M, Meurers T, Wirth F, Haber A, Müller A, Halilovic M, Balzer F, Prasser F. Data Provenance in Biomedical Research: Scoping Review. Journal of Medical Internet Research 2023;25:e42289 View
  3. Tseng Y, Chen C, Chang C. lab: an R package for generating analysis-ready data from laboratory records. PeerJ Computer Science 2023;9:e1528 View
  4. El Emam K, Leung T, Malin B, Klement W, Eysenbach G. Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS). Journal of Medical Internet Research 2024;26:e52508 View
  5. Kervezee L, Dashti H, Pilz L, Skarke C, Ruben M, Forger D. Using routinely collected clinical data for circadian medicine: A review of opportunities and challenges. PLOS Digital Health 2024;3(5):e0000511 View
  6. Naderian S, Rahmani R, Samad-Soltani T. A Natural Language Processing Framework for Detecting Adverse Drug Reactions in Clinical Structured Drug Reviews. International Journal of Drug Research in Clinics 2024;2:e9 View
  7. de Kok J, van Bussel B, van der Horst I, van Rosmalen F. From introducing Table 0 to forgetting Figure 1: it is time for a reporting guideline for publishing with real-world clinical data. Author's reply. Journal of Clinical Epidemiology 2025;179:111656 View
  8. Hu X, Zhi S, Li Y, Cheng Y, Fan H, Li H, Meng Z, Xie J, Tang S, Li W. Development and application of an early prediction model for risk of bloodstream infection based on real-world study. BMC Medical Informatics and Decision Making 2025;25(1) View
  9. Alhussan A, El-Kenawy E, Khafaga D, Eid M. Enhancing sleep disorder classification using machine learning and metaheuristic optimization techniques. Biomedical Signal Processing and Control 2025;110:108204 View
  10. Pal G. Improving personalized healthcare with automated longitudinal EHR analysis. International Journal of Medical Informatics 2025;196:106010 View

Books/Policy Documents

  1. Maletzky A, Kaltenleithner S, Moser P, Giretzlehner M. Artificial Intelligence Applications and Innovations. View