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

Foundations of AI (57) Artificial Intelligence (2140) Research Methodology - AI (38)

Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41264, first published .
Developing an Inpatient Electronic Medical Record Phenotype for Hospital-Acquired Pressure Injuries: Case Study Using Natural Language Processing Models

Developing an Inpatient Electronic Medical Record Phenotype for Hospital-Acquired Pressure Injuries: Case Study Using Natural Language Processing Models

Developing an Inpatient Electronic Medical Record Phenotype for Hospital-Acquired Pressure Injuries: Case Study Using Natural Language Processing Models

Authors of this article:

Elvira Nurmambetova1 Author Orcid Image ;   Jie Pan1, 2 Author Orcid Image ;   Zilong Zhang1 Author Orcid Image ;   Guosong Wu1, 2 Author Orcid Image ;   Seungwon Lee1, 2, 3 Author Orcid Image ;   Danielle A Southern1, 2 Author Orcid Image ;   Elliot A Martin1, 3 Author Orcid Image ;   Chester Ho4 Author Orcid Image ;   Yuan Xu1, 2, 5, 6 Author Orcid Image ;   Cathy A Eastwood1, 2 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

Journals

  1. Pan J, Zhang Z, Peters S, Vatanpour S, Walker R, Lee S, Martin E, Quan H. Cerebrovascular disease case identification in inpatient electronic medical record data using natural language processing. Brain Informatics 2023;10(1) View
  2. Pilowsky J, Choi J, Nguyen N, Williams L, Jones S. Pressure injury surveillance in the intensive care unit: Development, validation, and clinical application of a natural language processing algorithm. Australian Critical Care 2026;39(1):101487 View

Conference Proceedings

  1. Pan J, Lee S, Cheligeer C, Martin E, Riazi K, Quan H, Li N. 2024 IEEE International Conference on Digital Health (ICDH). Enhancing Large Language Models with Human Expertise for Disease Detection in Electronic Health Records View