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

Machine Learning (1465) Clinical Information and Decision Making (1407) Natural Language Processing (692) Decision Support for Health Professionals (1185)

Published on in Vol 10, No 5 (2022): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38241, first published .
Predicting Postoperative Mortality With Deep Neural Networks and Natural Language Processing: Model Development and Validation

Predicting Postoperative Mortality With Deep Neural Networks and Natural Language Processing: Model Development and Validation

Predicting Postoperative Mortality With Deep Neural Networks and Natural Language Processing: Model Development and Validation

Authors of this article:

Pei-Fu Chen1, 2 Author Orcid Image ;   Lichin Chen3 Author Orcid Image ;   Yow-Kuan Lin2, 4 Author Orcid Image ;   Guo-Hung Li2 Author Orcid Image ;   Feipei Lai2, 5, 6 Author Orcid Image ;   Cheng-Wei Lu1, 7 Author Orcid Image ;   Chi-Yu Yang8, 9 Author Orcid Image ;   Kuan-Chih Chen2, 10 Author Orcid Image ;   Tzu-Yu Lin1, 7 Author Orcid Image
Article Authors Cited by (10) Tweetations (4) Metrics

Journals

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  2. Abbasi N, Lacson R, Kapoor N, Licaros A, Guenette J, Burk K, Hammer M, Desai S, Eappen S, Saini S, Khorasani R. Development and External Validation of an Artificial Intelligence Model for Identifying Radiology Reports Containing Recommendations for Additional Imaging. American Journal of Roentgenology 2023;221(3):377 View
  3. Fitzpatrick N, Dobson R, Roberts A, Jones K, Shah A, Nenadic G, Ford E. Understanding Views Around the Creation of a Consented, Donated Databank of Clinical Free Text to Develop and Train Natural Language Processing Models for Research: Focus Group Interviews With Stakeholders. JMIR Medical Informatics 2023;11:e45534 View
  4. Arina P, Kaczorek M, Hofmaenner D, Pisciotta W, Refinetti P, Singer M, Mazomenos E, Whittle J. Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools. Anesthesiology 2024;140(1):85 View
  5. Rohatgi N. JMIR Perioperative Medicine: A Global Journal for Publishing Interdisciplinary Innovations, Research, and Perspectives. JMIR Perioperative Medicine 2023;6:e54344 View
  6. Huber M, Bello C, Schober P, Filipovic M, Luedi M. Decision Curve Analysis of In-Hospital Mortality Prediction Models: The Relative Value of Pre- and Intraoperative Data For Decision-Making. Anesthesia & Analgesia 2024;139(3):617 View
  7. Siebra C, Kurpicz-Briki M, Wac K. Transformers in health: a systematic review on architectures for longitudinal data analysis. Artificial Intelligence Review 2024;57(2) View
  8. Brydges G, Uppal A, Gottumukkala V. Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians. Current Oncology 2024;31(5):2727 View
  9. Chung P, Fong C, Walters A, Aghaeepour N, Yetisgen M, O’Reilly-Shah V. Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication. JAMA Surgery 2024;159(8):928 View
  10. Kong A, Liu N, Tan H, Sia A, Sng B. Artificial intelligence in obstetric anaesthesiology – The future of patient care?. International Journal of Obstetric Anesthesia 2024:104288 View