ArticleHealthcare informatics research2018
Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System.
Article in Healthcare informatics research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Multi-institutional model to predict intensive care unit length of stay after cardiac surgery.The Journal of thoracic and cardiovascular surgery · 2025Article
- A comparative study of neuro-fuzzy and neural network models in predicting length of stay in university hospital.BMC health services research · 2025Article
- Translational artificial intelligence-led optimization and realization of estimated discharge with a supportive weekend interprofessional flow team (TAILORED-SWIFT).Internal and emergency medicine · 2024Article
- Derivation and Validation of a Clinical Model to Predict Intensive Care Unit Length of Stay After Cardiac Surgery.Journal of the American Heart Association · 2020Article
- Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study.Internal and emergency medicine · 2020Article
- Security Risk Assessment of Healthcare Web Application Through Adaptive Neuro-Fuzzy Inference System: A Design Perspective.Risk management and healthcare policy · 2020Article
- The Role of Artificial Intelligence in the Prediction of Functional Maturation of Arteriovenous Fistula.Annals of vascular diseases · 2019Article
- Improving length of stay prediction using a hidden Markov model.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
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Authors and funding
4 authors.
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Abstract
objectivesAccurate prediction of patients' length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients' length of stay in intensive care units (ICU) after cardiac surgery.
methodsA cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016. Following the initial processing of influential factors, models were created and evaluated.
resultsThe results showed that the adaptive neuro-fuzzy algorithm (with mean squared error [MSE] = 7 and R = 0.88) resulted in the creation of a more precise model than the artificial neural network (with MSE = 21 and R = 0.60).
conclusionsThe adaptive neuro-fuzzy algorithm produces a more accurate model as it applies both the capabilities of a neural network architecture and experts' knowledge as a hybrid algorithm. It identifies nonlinear components, yielding remarkable results for prediction the length of stay, which is a useful calculation output to support ICU management, enabling higher quality of administration and cost reduction.
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