Published on 08.03.2023 in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41264, first published July 24, 2022.

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

Elvira Nurmambetova¹

; Jie Pan^{1, 2}

; Zilong Zhang¹

; Guosong Wu^{1, 2}

; Seungwon Lee^{1, 2, 3}

; Danielle A Southern^{1, 2}

; Elliot A Martin^{1, 3}

; Chester Ho⁴

; Yuan Xu^{1, 2, 5, 6}

; Cathy A Eastwood^{1, 2}

Article Authors Cited by (2) Tweetations (2) Metrics

Journals

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

Conference Proceedings

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