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

Machine Learning (1984) New Methods (1105) Electronic Health Records (1276) Decision Support for Health Professionals (1479)

Published on in Vol 10, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37658, first published .
Predicting Abnormalities in Laboratory Values of Patients in the Intensive Care Unit Using Different Deep Learning Models: Comparative Study

Predicting Abnormalities in Laboratory Values of Patients in the Intensive Care Unit Using Different Deep Learning Models: Comparative Study

Predicting Abnormalities in Laboratory Values of Patients in the Intensive Care Unit Using Different Deep Learning Models: Comparative Study

Authors of this article:

Ahmad Ayad1 Author Orcid Image ;   Ahmed Hallawa2 Author Orcid Image ;   Arne Peine2 Author Orcid Image ;   Lukas Martin2 Author Orcid Image ;   Lejla Begic Fazlic3 Author Orcid Image ;   Guido Dartmann3 Author Orcid Image ;   Gernot Marx2 Author Orcid Image ;   Anke Schmeink1 Author Orcid Image
Article Authors Cited by (8) Tweetations (3) Metrics

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  1. Chen J, Stumpe M, Cohen E. Evolving From Discrete Molecular Data Integrations to Actionable Molecular Insights Within the Electronic Health Record. JCO Clinical Cancer Informatics 2024;(8) View
  2. Bashiri F, Carey K, Martin J, Koyner J, Edelson D, Gilbert E, Mayampurath A, Afshar M, Churpek M. Development and external validation of deep learning clinical prediction models using variable-length time series data. Journal of the American Medical Informatics Association 2024;31(6):1322 View
  3. Primavesi F, Senoner T, Schindler S, Nikolajevic A, Di Fazio P, Csukovich G, Eller S, Neumayer B, Anliker M, Braunwarth E, Oberhuber R, Resch T, Maglione M, Cardini B, Niederwieser T, Gasteiger S, Klieser E, Tilg H, Schneeberger S, Neureiter D, Öfner D, Troppmair J, Stättner S. The Interplay between Perioperative Oxidative Stress and Hepatic Dysfunction after Human Liver Resection: A Prospective Observational Pilot Study. Antioxidants 2024;13(5):590 View
  4. Ayad A, Tai Y, Dartmann G, Schmeink A. Smart Summary: A Distributed Medical Recommender System for Patients in the ICU Using Neural Networks. IEEE Access 2024;12:83719 View
  5. Lin Y, K. Mensah I, Doering M, C. Shean R, Spies N. Machine learning-based error detection in the clinical laboratory: a critical review. Critical Reviews in Clinical Laboratory Sciences 2025:1 View
  6. Xie Y, Xie N, Guo J. Methodological development study: Dynamic mask attention graph neural network for mechanical ventilation in elderly intensive care unit patients. DIGITAL HEALTH 2025;11 View

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  1. Barhoush M, Ayad A, Schmeink A. 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). Accelerating Federated Learning via Modified Local Model Update Based on Individual Performance Metric View