ArticleScientific reports2021
Designing a bed-side system for predicting length of stay in a neonatal intensive care unit.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Development and external validation of the NEO-READY model to predict date of discharge among premature neonatal intensive care patients.Journal of perinatology : official journal of the California Perinatal Association · 2026Article
- Edge-Hosted LLM-Assisted NICU Discharge Summary Generation: Field-Level Evaluation Using a Clinician-Defined Rubric.Healthcare (Basel, Switzerland) · 2026Article
- Prediction of Neonatal Length of Stay in High-Risk Pregnancies Using Regression-Based Machine Learning on Computerized Cardiotocography Data.Diagnostics (Basel, Switzerland) · 2025Article
- Review
- Development and validation of a novel risk classification tool for predicting long length of stay in NICU blood transfusion infants.Scientific reports · 2024Article
- Extracellular Vesicle-microRNAs as Diagnostic Biomarkers in Preterm Neonates.International journal of molecular sciences · 2023Review
- Risk factors for length of NICU stay of newborns: A systematic review.Frontiers in pediatrics · 2023Review
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Increased length of stay (LOS) in intensive care units is directly associated with the financial burden, anxiety, and increased mortality risks. In the current study, we have incorporated the association of day-to-day nutrition and medication data of the patient during its stay in hospital with its predicted LOS. To demonstrate the same, we developed a model to predict the LOS using risk factors (a) perinatal and antenatal details, (b) deviation of nutrition and medication dosage from guidelines, and (c) clinical diagnoses encountered during NICU stay. Data of 836 patient records (12 months) from two NICU sites were used and validated on 211 patient records (4 months). A bedside user interface integrated with EMR has been designed to display the model performance results on the validation dataset. The study shows that each gestation age group of patients has unique and independent risk factors associated with the LOS. The gestation is a significant risk factor for neonates < 34 weeks, nutrition deviation for < 32 weeks, and clinical diagnosis (sepsis) for ≥ 32 weeks. Patients on medications had considerable extra LOS for ≥ 32 weeks' gestation. The presented LOS model is tailored for each patient, and deviations from the recommended nutrition and medication guidelines were significantly associated with the predicted LOS.
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Registered trials
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