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

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
  7. Devlin J, Sieber F, Akeju O, Khan B, MacLullich A, Marcantonio E, Oh E, Agar M, Avelino-Silva T, Berger M, Burry L, Colantuoni E, Evered L, Girard T, Han J, Hosie A, Hughes C, Jones R, Pandharipande P, Subramanian B, Travison T, van den Boogaard M, Inouye S. Advancing Delirium Treatment Trials in Older Adults: Recommendations for Future Trials From the Network for Investigation of Delirium: Unifying Scientists (NIDUS). Critical Care Medicine 2025;53(1):e15 View
  8. Xu Y, Min M, Tan L. Regarding: the interplay of delirium and frailty in hospitalized older adults. Journal of Internal Medicine 2025;297(4):448 View
  9. Lv S, Li J, He H, Zhao Q, Jiang Y. Artificial intelligence applications in delirium prediction, diagnosis, and management: a systematic review. Artificial Intelligence Review 2025;58(9) 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

Conference Proceedings

  1. Chen A, Paredes D, Yu Z, Lou X, Brunson R, Thomas J, Martinez K, Lucero R, Magoc T, Solberg L, Snigurska U, Ser S, Prosperi M, Bian J, Bjarnadottir R, Wu Y. 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI). Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing View