Development and validation of machine learning-based clinical decision support tool for identifying malnutrition in NICU patients

Autor: Nadir Yalçın, Merve Kaşıkcı, Hasan Tolga Çelik, Kutay Demirkan, Şule Yiğit, Murat Yurdakök

Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-10 (2023)

Abstract Hospitalized newborns have an increased risk of malnutrition and, especially preterm infants, often experience malnutrition-related extrauterine growth restriction (EUGR). The aim of this study was to predict the discharge weight and the pre

Externí odkaz: https://doaj.org/article/592f6bc3ac624953a6983eeb9cca9e6a

Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit

Autor: Nadir Yalçın, Merve Kaşıkcı, Hasan Tolga Çelik, Karel Allegaert, Kutay Demirkan, Şule Yiğit, Murat Yurdakök

Publikováno v: Frontiers in Pharmacology, Vol 14 (2023)

Aim: To develop models that predict the presence of medication errors (MEs) (prescription, preparation, administration, and monitoring) using machine learning in NICU patients.Design: Prospective, observational cohort study randomized with machine le

Externí odkaz: https://doaj.org/article/b866cdc65fe548fc859be978c1527a06

Validity and reliability of the MetabQoL 1.0 and assessment of neuropsychiatric burden in organic acidemias: Reflections from Turkey

Autor: Ersak, Ayşe Şenol, Çak, Halime Tuna, Yıldız, Yılmaz, Çavdar, Merve Kaşıkcı, Tunç, Sıla, Özer, Nagihan, Zeltner, Nina A., Huemer, Martina, Tokatlı, Ayşegül, Haliloğlu, Göknur

Publikováno v: In Molecular Genetics and Metabolism January 2024 141(1)

Novel Method for Early Prediction of Clinically Significant Drug-Drug Interactions with a Machine Learning Algorithm Based on Risk Matrix Analysis in the NICU

Autor: Nadir Yalçın, Merve Kaşıkcı, Hasan Tolga Çelik, Karel Allegaert, Kutay Demirkan, Şule Yiğit, Murat Yurdakök

Publikováno v: Journal of Clinical Medicine; Volume 11; Issue 16; Pages: 4715
Journal of Clinical Medicine, 11(16):4715. Multidisciplinary Digital Publishing Institute (MDPI)

Aims: Evidence for drug-drug interactions (DDIs) that may cause age-dependent differences in the incidence and severity of adverse drug reactions (ADRs) in newborns is sparse. We aimed to develop machine learning (ML) algorithms that predict DDI pres

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1c3a47a29eca28f498c8155f2e0cc4cd
https://pubmed.ncbi.nlm.nih.gov/36012954