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

Development and Evaluation of Research Methods, Instruments and Tools (481) Natural Language Processing (692) Ontologies, Classifications, and Coding (239) Electronic Health Records (1053)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43014, first published .
Extracting Medical Information From Free-Text and Unstructured Patient-Generated Health Data Using Natural Language Processing Methods: Feasibility Study With Real-world Data

Extracting Medical Information From Free-Text and Unstructured Patient-Generated Health Data Using Natural Language Processing Methods: Feasibility Study With Real-world Data

Extracting Medical Information From Free-Text and Unstructured Patient-Generated Health Data Using Natural Language Processing Methods: Feasibility Study With Real-world Data

Authors of this article:

Emre Sezgin1, 2 Author Orcid Image ;   Syed-Amad Hussain1 Author Orcid Image ;   Steve Rust1 Author Orcid Image ;   Yungui Huang1 Author Orcid Image
Article Authors Cited by (17) Tweetations (9) Metrics

Journals

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  2. Stasevych M, Zvarych V. Innovative Robotic Technologies and Artificial Intelligence in Pharmacy and Medicine: Paving the Way for the Future of Health Care—A Review. Big Data and Cognitive Computing 2023;7(3):147 View
  3. Leviton A, Loddenkemper T. Design, implementation, and inferential issues associated with clinical trials that rely on data in electronic medical records: a narrative review. BMC Medical Research Methodology 2023;23(1) View
  4. Soto Jacome C, Segura Torres D, Fan J, Loor-Torres R, Duran M, Al Zahidy M, Cabezas E, Borras-Osorio M, Toro-Tobon D, Wu Y, Wu Y, Singh Ospina N, Brito J. Thyroid Ultrasound Appropriateness Identification Through Natural Language Processing of Electronic Health Records. Mayo Clinic Proceedings: Digital Health 2024;2(1):67 View
  5. Shalfeeva E, Gribova V. The Issues of Creation of Machine-Understandable Smart Standards Based on Knowledge Graphs. Informatics and Automation 2024;23(4):969 View
  6. Filetti S, Fenza G, Gallo A. Research design and writing of scholarly articles: new artificial intelligence tools available for researchers. Endocrine 2024;85(3):1104 View
  7. Luo M, Trivedi S, Kurian A, Ward K, Keegan T, Rubin D, Banerjee I. Automated Extraction of Patient-Centered Outcomes After Breast Cancer Treatment: An Open-Source Large Language Model–Based Toolkit. JCO Clinical Cancer Informatics 2024;(8) View
  8. Tabaie A, Tran A, Calabria T, Bennett S, Milicia A, Weintraub W, Gallagher W, Yosaitis J, Schubel L, Hill M, Smith K, Miller K. Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study. Journal of Medical Internet Research 2024;26:e50935 View
  9. Chang E, Sung S. Use of SNOMED CT in Large Language Models: Scoping Review. JMIR Medical Informatics 2024;12:e62924 View
  10. Pooryousef V, Cordeil M, Besançon L, Bassed R, Dwyer T. Collaborative Forensic Autopsy Documentation and Supervised Report Generation Using a Hybrid Mixed-Reality Environment and Generative AI. IEEE Transactions on Visualization and Computer Graphics 2024;30(11):7452 View
  11. Kawasaki Y, Nii M, Nishioka E. Nursing Records Regarding Decision-Making in Cancer Supportive Care: A Retrospective Study in Japan. Healthcare Informatics Research 2024;30(4):364 View

Books/Policy Documents

  1. Afşin Y, Taşkaya Temizel T. Persuasive Technology. View
  2. Chaudhari A. Future of AI in Biomedicine and Biotechnology. View
  3. Dorrn T, Müller A. Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3. View