Machine Learning for the Identification of Key Predictors to Bayley Outcomes: A Preterm Cohort Study
The application of machine learning in predicting neurodevelopmental outcomes of preterm infants represents a significant advancement in neonatal care. The integration of machine learning models with clinical workflows requires ongoing education and collaboration between data scientists and healthcare professionals to ensure the models' practical applicability and interpretability.
Authors
- Ann‐Marie Malby Schoos (ORCID: https://orcid.org/0000-0002-5827-0885)
- Nina Barišić (ORCID: https://orcid.org/0000-0002-8839-1522)
- Branka Bunoza (ORCID: https://orcid.org/0009-0009-1598-3760)
- Iva Šunić (ORCID: https://orcid.org/0000-0002-5289-9463)
- Mario Lovrić (ORCID: https://orcid.org/0000-0002-3541-9624)
- Petra Grđan Stevanović
- Ruža Grizelj (ORCID: https://orcid.org/0000-0001-6077-9878)
- Ana Bogdanić
- Ivan Jovanović (ORCID: https://orcid.org/0000-0002-1248-1345)
Institutions
- University of Copenhagen (DK)
- University Hospital Centre Zagreb (HR)
- University of Zagreb (HR)
- Gentofte Hospital (DK)
- Institute for Anthropological Research (HR)
- Slagelse Hospital (DK)
- Copenhagen Prospective Studies on Asthma in Childhood (DK)
Publication Details
- Journal
- Journal of Personalized Medicine
- Published
- 2024-08-30
- DOI
- https://doi.org/10.3390/jpm14090922
- Citations
- 1
- Primary Topic
- Neonatal Respiratory Health Research
- Type
- article
- Field-Weighted Citation Impact
- 0.42