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

Methods and Feasibility Studies (451) Clinical Information and Decision Making (1915) Electronic Health Records (1319) Decision Support for Health Professionals (1539) Machine Learning (2076) Clinical Informatics (1325) Secondary Use of Clinical Data for Research and Surveillance (474) Artificial Intelligence (2129)

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 (24) 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
  13. Lin C, Lin E, Lane H. Interpretable machine learning to evaluate relationships between DAO/DAOA (pLG72) protein data and features in clinical assessments, functional outcome, and cognitive function in schizophrenia patients. Schizophrenia 2025;11(1) View
  14. Seo W, Li J, Zhang Z, Zheng C, Singh H, Pasupathy K, Mahajan P, Park S. Designing Health Care Provider–Centered Emergency Department Interventions: Participatory Design Study. JMIR Formative Research 2025;9:e68891 View
  15. Halwani M, Merdad G, Almasre M, Doman G, AlSharif S, Alshiakh S, Mahboob D, Halwani M, Faqerah N, Mosuily M. Predicting triage of pediatric patients in the emergency department using machine learning approach. International Journal of Emergency Medicine 2025;18(1) View
  16. Ming C, Lee G, Teo Y, Teo Y, Zhou X, Ho E, Toh E, Ong M, Tan B, Ho A. Deep learning modelling to forecast emergency department visits using calendar, meteorological, internet search data and stock market price. Computer Methods and Programs in Biomedicine 2025;267:108808 View
  17. Tay J, Okada Y, Nadarajan G, Siddiqui F, Barry T, Ong M. Pragmatic Risk Stratification Method to Identify Emergency Department Presentations for Alternative Care Service Pathways: Registry-Based Retrospective Study Over 5 Years. Journal of Medical Internet Research 2025;27:e73758 View
  18. Khandelwal P, Okada Y, Ning Y, Hu Z, Ho A, Tan K, Ong M. Association between age and length of stay in the emergency department in a tertiary care hospital: a retrospective observational study. Emergency Medicine Journal 2025:emermed-2024-214299 View
  19. Siddiqui F, Kumar A, Ansah J, Yuan G, Liu Z, Malhotra R, Ong M, Lam S. Long-Range Forecasting for Emergency Care Systems in a Highly Dynamic Setting: A Singapore Case Study. JACEP Open 2025;6(4):100184 View
  20. Li S, Wang Z, Shang Y, Wu Q, Hong C, Ning Y, Miao D, Ong M, Chakraborty B, Liu N. Developing federated time-to-event scores using heterogeneous real-world survival data. Computers in Biology and Medicine 2025;197:111084 View
  21. Blythe R, Parsons R, Ong M, Barnett A. Continuous predicted risks should be retained when deploying clinical prediction models. Journal of Clinical Epidemiology 2025;188:112009 View
  22. Heydari Dehaghani S, Rasouli M. An Interpretable Predictive Process Monitoring Approach To Estimate the Improvement Status of Emergency Patients Using Vital Signs. SN Comprehensive Clinical Medicine 2025;7(1) View

Books/Policy Documents

  1. Mittal H, Diwakar A, Tomer V, Chaudhary S. Demystifying Emerging Trends in Green Technology. View
  2. Hamza A, Ilyas M. Pattern Recognition and Artificial Intelligence. View