Published on in Vol 24, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40384, first published .
Development and Evaluation of a Natural Language Processing Annotation Tool to Facilitate Phenotyping of Cognitive Status in Electronic Health Records: Diagnostic Study

Development and Evaluation of a Natural Language Processing Annotation Tool to Facilitate Phenotyping of Cognitive Status in Electronic Health Records: Diagnostic Study

Development and Evaluation of a Natural Language Processing Annotation Tool to Facilitate Phenotyping of Cognitive Status in Electronic Health Records: Diagnostic Study

Journals

  1. Oh I, Schindler S, Ghoshal N, Lai A, Payne P, Gupta A. Extraction of clinical phenotypes for Alzheimer’s disease dementia from clinical notes using natural language processing. JAMIA Open 2023;6(1) View
  2. Wilcox D, Rudmann E, Ye E, Noori A, Magdamo C, Jain A, Alabsi H, Foy B, Triant V, Robbins G, Westover M, Das S, Mukerji S. Cognitive concerns are a risk factor for mortality in people with HIV and coronavirus disease 2019. AIDS 2023;37(10):1565 View
  3. Lynch D, Lynch M, Wessell K, Kistler C, Hanson L. Performance of an embedded algorithm to identify people with dementia in a clinical trial. Journal of the American Geriatrics Society 2023;71(11):3647 View
  4. Panesar K, Pérez Cabello de Alba M. Natural language processing-driven framework for the early detection of language and cognitive decline. Language and Health 2023;1(2):20 View
  5. Aizenstein H, Moore R, Vahia I, Ciarleglio A. Deep Learning and Geriatric Mental Health. The American Journal of Geriatric Psychiatry 2024;32(3):270 View
  6. Ryvicker M, Barrón Y, Song J, Zolnoori M, Shah S, Burgdorf J, Noble J, Topaz M. Using Natural Language Processing to Identify Home Health Care Patients at Risk for Diagnosis of Alzheimer’s Disease and Related Dementias. Journal of Applied Gerontology 2024 View