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

Formative Evaluation of Digital Health Interventions (2415) Secondary Use of Clinical Data for Research and Surveillance (367) Natural Language Processing (693) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1186) Electronic Health Records (1053)

Published on in Vol 6, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33834, first published .
Identifying Patients With Delirium Based on Unstructured Clinical Notes: Observational Study

Identifying Patients With Delirium Based on Unstructured Clinical Notes: Observational Study

Identifying Patients With Delirium Based on Unstructured Clinical Notes: Observational Study

Authors of this article:

Wendong Ge1 Author Orcid Image ;   Haitham Alabsi1 Author Orcid Image ;   Aayushee Jain1 Author Orcid Image ;   Elissa Ye1 Author Orcid Image ;   Haoqi Sun1 Author Orcid Image ;   Marta Fernandes1 Author Orcid Image ;   Colin Magdamo1 Author Orcid Image ;   Ryan A Tesh1 Author Orcid Image ;   Sarah I Collens1 Author Orcid Image ;   Amy Newhouse1 Author Orcid Image ;   Lidia MVR Moura1 Author Orcid Image ;   Sahar Zafar1 Author Orcid Image ;   John Hsu1 Author Orcid Image ;   Oluwaseun Akeju1 Author Orcid Image ;   Gregory K Robbins1 Author Orcid Image ;   Shibani S Mukerji1 Author Orcid Image ;   Sudeshna Das1 Author Orcid Image ;   M Brandon Westover1 Author Orcid Image
Article Authors Cited by (8) Tweetations (4) Metrics

Journals

  1. Qu J, Mueller A, McKay T, Westover M, Shelton K, Shaefi S, D'Alessandro D, Berra L, Brown E, Houle T, Akeju O. Nighttime dexmedetomidine for delirium prevention in non-mechanically ventilated patients after cardiac surgery (MINDDS): a single-centre, parallel-arm, randomised, placebo-controlled superiority trial. eClinicalMedicine 2023;56:101796 View
  2. Ser S, Shear K, Snigurska U, Prosperi M, Wu Y, Magoc T, Bjarnadottir R, Lucero R. Clinical Prediction Models for Hospital-Induced Delirium Using Structured and Unstructured Electronic Health Record Data: Protocol for a Development and Validation Study. JMIR Research Protocols 2023;12:e48521 View
  3. Young M, Holmes N, Kishore K, Amjad S, Gaca M, Serpa Neto A, Reade M, Bellomo R. Natural language processing diagnosed behavioural disturbance phenotypes in the intensive care unit: characteristics, prevalence, trajectory, treatment, and outcomes. Critical Care 2023;27(1) View
  4. Miyazawa Y, Katsuta N, Nara T, Nojiri S, Naito T, Hiki M, Ichikawa M, Takeshita Y, Kato T, Okumura M, Tobita M, Jakovljevic M. Identification of risk factors for the onset of delirium associated with COVID-19 by mining nursing records. PLOS ONE 2024;19(1):e0296760 View
  5. Amjad S, Holmes N, Kishore K, Young M, Bailey J, Bellomo R, Verspoor K. Advancing delirium classification: A clinical notes-based natural language processing-supported machine learning model. Intelligence-Based Medicine 2024;9:100140 View
  6. Osman M, Cooper R, Sayer A, Witham M. The use of natural language processing for the identification of ageing syndromes including sarcopenia, frailty and falls in electronic healthcare records: a systematic review. Age and Ageing 2024;53(7) View

Books/Policy Documents

  1. Guellil I, Andres S, Guthrie B, Anand A, Zhang H, Hasan A, Wu H, Alex B. Natural Language Processing and Information Systems. View