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

Methods and Feasibility Studies (383) Clinical Information and Decision Making (1423) Electronic Health Records (1065) Decision Support for Health Professionals (1199) Machine Learning (1485) Clinical Informatics (1042) Secondary Use of Clinical Data for Research and Surveillance (373) Artificial Intelligence (1400)

Published on in Vol 11, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34201, first published .
Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation

Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation

Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation

Authors of this article:

Nan Liu1, 2, 3, 4 Author Orcid Image ;   Feng Xie1 Author Orcid Image ;   Fahad Javaid Siddiqui1 Author Orcid Image ;   Andrew Fu Wah Ho1, 5 Author Orcid Image ;   Bibhas Chakraborty1, 6, 7 Author Orcid Image ;   Gayathri Devi Nadarajan5 Author Orcid Image ;   Kenneth Boon Kiat Tan5 Author Orcid Image ;   Marcus Eng Hock Ong1, 4, 5 Author Orcid Image
Article Authors Cited by (12) Tweetations (2) Metrics

Journals

  1. Xie F, Zhou J, Lee J, Tan M, Li S, Rajnthern L, Chee M, Chakraborty B, Wong A, Dagan A, Ong M, Gao F, Liu N. Benchmarking emergency department prediction models with machine learning and public electronic health records. Scientific Data 2022;9(1) View
  2. Saffari S, Ning Y, Xie F, Chakraborty B, Volovici V, Vaughan R, Ong M, Liu N. AutoScore-Ordinal: an interpretable machine learning framework for generating scoring models for ordinal outcomes. BMC Medical Research Methodology 2022;22(1) View
  3. Joyce C, Markossian T, Nikolaides J, Ramsey E, Thompson H, Rojas J, Sharma B, Dligach D, Oguss M, Cooper R, Afshar M. The Evaluation of a Clinical Decision Support Tool Using Natural Language Processing to Screen Hospitalized Adults for Unhealthy Substance Use: Protocol for a Quasi-Experimental Design. JMIR Research Protocols 2022;11(12):e42971 View
  4. Tsai W, Liu C, Lin H, Hsu C, Ma Y, Chen C, Huang C, Chen C. Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients. Healthcare 2022;10(8):1498 View
  5. Lo J, Tromp J, Ouwerkwerk W, Ong M, Tan K, Sim D, Graves N. Examining predictors for 6-month mortality and healthcare utilization for patients admitted for heart failure in the acute care setting. International Journal of Cardiology 2023;390:131237 View
  6. Iqbal U, Prentice W, Lawler A. Digital health in Tasmania – improving patient access and outcomes. BMJ Health & Care Informatics 2023;30(1):e100802 View
  7. Ricciardi C, Marino M, Trunfio T, Majolo M, Romano M, Amato F, Improta G. Evaluation of different machine learning algorithms for predicting the length of stay in the emergency departments: a single-centre study. Frontiers in Digital Health 2024;5 View
  8. Okada Y, Ning Y, Ong M. Explainable artificial intelligence in emergency medicine: an overview. Clinical and Experimental Emergency Medicine 2023;10(4):354 View
  9. Okada Y, Aik J, Ho A, Ning Y, Ong M. Heat-related illness in Singapore: Descriptive analysis of a tertiary care center from 2008 to 2020. Proceedings of Singapore Healthcare 2024;33 View
  10. Ho K. Digitisation of emergency medicine: opportunities, examples and issues for consideration. Singapore Medical Journal 2024;65(3):179 View
  11. Liu M, Ning Y, Ke Y, Shang Y, Chakraborty B, Ong M, Vaughan R, Liu N. FAIM: Fairness-aware interpretable modeling for trustworthy machine learning in healthcare. Patterns 2024;5(10):101059 View
  12. Li S, Miao D, Wu Q, Hong C, D’Agostino D, Li X, Ning Y, Shang Y, Wang Z, Liu M, Fu H, Ong M, Haddadi H, Liu N. Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data Analysis. Health Data Science 2024;4 View