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

Theme Issue 2022: Health Natural Language Processing and Applications (CHIP) (27) Natural Language Processing (650) Machine Learning (1401) Decision Support for Health Professionals (1147) Artificial Intelligence (1216)

Published on in Vol 10, No 12 (2022): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38161, first published .
Boosting Delirium Identification Accuracy With Sentiment-Based Natural Language Processing: Mixed Methods Study

Boosting Delirium Identification Accuracy With Sentiment-Based Natural Language Processing: Mixed Methods Study

Boosting Delirium Identification Accuracy With Sentiment-Based Natural Language Processing: Mixed Methods Study

Authors of this article:

Lu Wang1, 2 Author Orcid Image ;   Yilun Zhang1 Author Orcid Image ;   Mark Chignell1 Author Orcid Image ;   Baizun Shan1 Author Orcid Image ;   Kathleen A Sheehan3, 4 Author Orcid Image ;   Fahad Razak3, 5 Author Orcid Image ;   Amol Verma3, 5 Author Orcid Image
Article Authors Cited by (5) Tweetations (3) Metrics

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  3. Verma A, Trbovich P, Mamdani M, Shojania K. Grand rounds in methodology: key considerations for implementing machine learning solutions in quality improvement initiatives. BMJ Quality & Safety 2024;33(2):121 View
  4. Chen H, Cohen E, Wilson D, Alfred M. A Machine Learning Approach with Human-AI Collaboration for Automated Classification of Patient Safety Event Reports: Algorithm Development and Validation Study. JMIR Human Factors 2024;11:e53378 View
  5. Bartal A, Jagodnik K, Chan S, Dekel S. AI and narrative embeddings detect PTSD following childbirth via birth stories. Scientific Reports 2024;14(1) View