Ranking Nursing Diagnoses by Predictive Relevance for Intensive Care Unit Transfer Risk in Adult and Pediatric Patients: A Machine Learning Approach with Random Forest

Manuele Cesare; Mario Cesare Nurchis; Nursing and Public Health Group; Gianfranco Damiani; Antonello Cocchieri

doi:10.3390/healthcare13111339

Ranking Nursing Diagnoses by Predictive Relevance for Intensive Care Unit Transfer Risk in Adult and Pediatric Patients: A Machine Learning Approach with Random Forest

Manuele Cesare^*
, Mario Cesare Nurchis
, Nursing and Public Health Group
, Gianfranco Damiani
, Antonello Cocchieri

^*Corresponding author

Department of Research

Research output: Contribution to journal › Article

Abstract

Inglese

Original language	English
Pages (from-to)	N/A-N/A
Journal	HEALTHCARE
Volume	13
Issue number	11
DOIs	https://doi.org/10.3390/healthcare13111339
Publication status	Published - 2025

All Science Journal Classification (ASJC) codes

Leadership and Management
Health Policy
Health Informatics
Health Information Management

Keywords

adult
clinical deterioration
intensive care units
machine learning
nursing diagnosis
patient transfer
pediatrics
random forest

Access to Document

10.3390/healthcare13111339

Cite this

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title = "Ranking Nursing Diagnoses by Predictive Relevance for Intensive Care Unit Transfer Risk in Adult and Pediatric Patients: A Machine Learning Approach with Random Forest",

abstract = "Inglese",

keywords = "adult, clinical deterioration, intensive care units, machine learning, nursing diagnosis, patient transfer, pediatrics, random forest, adult, clinical deterioration, intensive care units, machine learning, nursing diagnosis, patient transfer, pediatrics, random forest",

author = "Manuele Cesare and Nurchis, \{Mario Cesare\} and Group, \{Nursing and Public Health\} and Gianfranco Damiani and Antonello Cocchieri",

year = "2025",

doi = "10.3390/healthcare13111339",

language = "English",

volume = "13",

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journal = "HEALTHCARE",

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AU - Cesare, Manuele

AU - Nurchis, Mario Cesare

AU - Group, Nursing and Public Health

AU - Damiani, Gianfranco

AU - Cocchieri, Antonello

PY - 2025

Y1 - 2025

N2 - Inglese

AB - Inglese

KW - adult

KW - clinical deterioration

KW - intensive care units

KW - machine learning

KW - nursing diagnosis

KW - patient transfer

KW - pediatrics

KW - random forest

KW - adult

KW - clinical deterioration

KW - intensive care units

KW - machine learning

KW - nursing diagnosis

KW - patient transfer

KW - pediatrics

KW - random forest

UR - https://publicatt.unicatt.it/handle/10807/316497

UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=105007770043&origin=inward

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U2 - 10.3390/healthcare13111339

DO - 10.3390/healthcare13111339

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VL - 13

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JF - HEALTHCARE

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Ranking Nursing Diagnoses by Predictive Relevance for Intensive Care Unit Transfer Risk in Adult and Pediatric Patients: A Machine Learning Approach with Random Forest

Abstract

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

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