Published on in Vol 7 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57926, first published .
Extracting Critical Information from Unstructured Clinicians’ Notes Data to Identify Dementia Severity Using a Rule-Based Approach: Feasibility Study

Extracting Critical Information from Unstructured Clinicians’ Notes Data to Identify Dementia Severity Using a Rule-Based Approach: Feasibility Study

Extracting Critical Information from Unstructured Clinicians’ Notes Data to Identify Dementia Severity Using a Rule-Based Approach: Feasibility Study

Journals

  1. Shankar R, Bundele A, Mukhopadhyay A. Natural language processing of electronic health records for early detection of cognitive decline: a systematic review. npj Digital Medicine 2025;8(1) View
  2. Cheng Y, Malekar M, He Y, Bommareddy A, Magdamo C, Singh A, Westover B, Mukerji S, Dickson J, Das S. High-Throughput Phenotyping of the Symptoms of Alzheimer Disease and Related Dementias Using Large Language Models: Cross-Sectional Study. JMIR AI 2025;4:e66926 View
  3. Weisenbach S. Empowering minds: revolutionizing cognitive and emotional health assessment with stepped care and digital tools. Journal of Clinical and Experimental Neuropsychology 2025;47(6):523 View
  4. Li R, Wang X, Berlowitz D, Mez J, Lin H, Yu H. CARE-AD: a multi-agent large language model framework for Alzheimer’s disease prediction using longitudinal clinical notes. npj Digital Medicine 2025;8(1) View
  5. Xiao Y, Pan Q, Liu H, He Y, Zhang Y, Jiang N. Evaluating large language models for mild cognitive impairment among older adults: A bilingual comparison of ChatGPT, Gemini, and Kimi. Health Informatics Journal 2025;31(3) View