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

Participatory Medicine & E-Patients (781) Natural Language Processing (704) Physician and Health Services Rating by Consumers (129) Electronic Health Records (1067) Ontologies, Classifications, and Coding (240)

Published on in Vol 20, No 1 (2018): January

A Natural Language Processing System That Links Medical Terms in Electronic Health Record Notes to Lay Definitions: System Development Using Physician Reviews

A Natural Language Processing System That Links Medical Terms in Electronic Health Record Notes to Lay Definitions: System Development Using Physician Reviews

A Natural Language Processing System That Links Medical Terms in Electronic Health Record Notes to Lay Definitions: System Development Using Physician Reviews

Authors of this article:

Jinying Chen1 Author Orcid Image ;   Emily Druhl2 Author Orcid Image ;   Balaji Polepalli Ramesh3 Author Orcid Image ;   Thomas K Houston1, 2 Author Orcid Image ;   Cynthia A Brandt4, 5 Author Orcid Image ;   Donna M Zulman6, 7 Author Orcid Image ;   Varsha G Vimalananda2, 8 Author Orcid Image ;   Samir Malkani9 Author Orcid Image ;   Hong Yu1, 2 Author Orcid Image
Article Authors Cited by (35) Tweetations (27) Metrics

Journals

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  8. Priyadarshi A, Saha S. Web Information Extraction for Finding Remedy Based on a Patient-Authored Text: A Study on Homeopathy. Network Modeling Analysis in Health Informatics and Bioinformatics 2020;9(1) View
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  11. Blok A, Amante D, Hogan T, Sadasivam R, Shimada S, Woods S, Nazi K, Houston T. Impact of Patient Access to Online VA Notes on Healthcare Utilization and Clinician Documentation: a Retrospective Cohort Study. Journal of General Internal Medicine 2021;36(3):592 View
  12. Lalor J, Hu W, Tran M, Wu H, Mazor K, Yu H. Evaluating the Effectiveness of NoteAid in a Community Hospital Setting: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Patients. Journal of Medical Internet Research 2021;23(5):e26354 View
  13. van Mens H, Martens S, Paiman E, Mertens A, Nienhuis R, de Keizer N, Cornet R. Diagnosis clarification by generalization to patient-friendly terms and definitions: Validation study. Journal of Biomedical Informatics 2022;129:104071 View
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  17. Kujala S, Hörhammer I, Väyrynen A, Holmroos M, Nättiaho-Rönnholm M, Hägglund M, Johansen M. Patients' Experiences of Web-Based Access to Electronic Health Records in Finland: Cross-sectional Survey. Journal of Medical Internet Research 2022;24(6):e37438 View
  18. Yilmaz Y, Jurado Nunez A, Ariaeinejad A, Lee M, Sherbino J, Chan T. Harnessing Natural Language Processing to Support Decisions Around Workplace-Based Assessment: Machine Learning Study of Competency-Based Medical Education. JMIR Medical Education 2022;8(2):e30537 View
  19. Keloth V, Zhou S, Lindemann L, Zheng L, Elhanan G, Einstein A, Geller J, Perl Y. Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients. BMC Medical Informatics and Decision Making 2023;23(S1) View
  20. Lalor J, Wu H, Mazor K, Yu H. Evaluating the efficacy of NoteAid on EHR note comprehension among US Veterans through Amazon Mechanical Turk. International Journal of Medical Informatics 2023;172:105006 View
  21. Li X, Zhang Y, Jin J, Sun F, Li N, Liang S, Ijaz M. A model of integrating convolution and BiGRU dual-channel mechanism for Chinese medical text classifications. PLOS ONE 2023;18(3):e0282824 View
  22. Tenorio J, de Moraes F, Pisa I. CHV.br: Exploratory study for the development of a consumer health vocabulary (CHV) supported by a network model for Brazilian Portuguese language. Journal of Information Science 2023 View
  23. van Mens H, Hannen G, Nienhuis R, Bolt R, de Keizer N, Cornet R. Evaluation of Patient-Friendly Diagnosis Clarifications in a Hospital Patient Portal. Applied Clinical Informatics 2023;14(03):455 View
  24. Nacht C, Jacobson N, Shiyanbola O, Smith C, Hoonakker P, Coller R, Dean S, Sklansky D, Smith W, Sprackling C, Kelly M. Perception of Physicians’ Notes Among Parents of Different Health Literacy Levels. Hospital Pediatrics 2024;14(2):108 View
  25. Deeb M, Gangadhar A, Rabindranath M, Rao K, Brudno M, Sidhu A, Wang B, Bhat M. The emerging role of generative artificial intelligence in transplant medicine. American Journal of Transplantation 2024;24(10):1724 View
  26. Lalor J, Levy D, Jordan H, Hu W, Smirnova J, Yu H. Evaluating Expert-Layperson Agreement in Identifying Jargon Terms in Electronic Health Record Notes: Observational Study. Journal of Medical Internet Research 2024;26:e49704 View
  27. Levy D, Jordan H, Lalor J, Smirnova J, Hu W, Liu W, Yu H. Individual factors that affect laypeople's understanding of definitions of medical jargon. Health Policy and Technology 2024;13(6):100932 View

Books/Policy Documents

  1. Lloyd N, Khuman A. Artificial Intelligence in Healthcare. View
  2. Ningtyas A, El-Ebshihy A, Herwanto G, Piroi F, Hanbury A. Experimental IR Meets Multilinguality, Multimodality, and Interaction. View
  3. Mishra M, Dubey V, Hackett T, Kashyap M. Artificial Intelligence and Machine Learning in Healthcare. View
  4. Ningtyas A, El-Ebshihy A, Piroi F, Hanbury A. Experimental IR Meets Multilinguality, Multimodality, and Interaction. View