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

^*Autore corrispondente per questo lavoro

Facoltà di Medicina e Chirurgia "A.Gemelli"
Ambito Disciplinare: STATISTICA MEDICA, IGIENE GENERALE E APPLICATA E SCIENZE INFERMIERISTICHE GENERALI, CLINICHE, PEDIATRICHE E OSTETRICO-GINECOLOGICHE E NEONATALI
Dipartimento di Scienze della vita e sanità pubblica

Department of Research

Risultato della ricerca: Contributo in rivista › Articolo

Abstract

Inglese

Lingua originale	Inglese
pagine (da-a)	N/A-N/A
Rivista	HEALTHCARE
Volume	13
Numero di pubblicazione	11
DOI	https://doi.org/10.3390/healthcare13111339
Stato di pubblicazione	Pubblicato - 2025

All Science Journal Classification (ASJC) codes

Leadership e Management
Politiche della Salute
Informatica della Salute
Gestione dell’Informazione sulla Salute

Keywords

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

Accesso al documento

10.3390/healthcare13111339

Altri file e collegamenti

Cita questo

@article{9af84931122c414db3925386b0d5ff91,

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",

pages = "N/A--N/A",

journal = "HEALTHCARE",

issn = "2227-9032",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

number = "11",

}

TY - JOUR

T1 - Ranking Nursing Diagnoses by Predictive Relevance for Intensive Care Unit Transfer Risk in Adult and Pediatric Patients: A Machine Learning Approach with Random Forest

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

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

U2 - 10.3390/healthcare13111339

DO - 10.3390/healthcare13111339

M3 - Article

SN - 2227-9032

VL - 13

SP - N/A-N/A

JO - HEALTHCARE

JF - HEALTHCARE

IS - 11

ER -

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

Accesso al documento

Altri file e collegamenti

Fingerprint

Cita questo