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

Development and Evaluation of Research Methods, Instruments and Tools (439) Natural Language Processing (650) Ontologies, Classifications, and Coding (226) Electronic Health Records (971)

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 (9) Tweetations (9) Metrics

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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

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