EARLY IDENTIFICATION OF VENTILATOR ASSOCIATED PNEUMONIA USING MACHINE LEARNING TECHNIQUES: A PROSPECTIVE COHORT STUDY

Forschungsstelle

INNOSUISSE

Projektnummer

102.522.1 IP-ICT

Projekttitel

EARLY IDENTIFICATION OF VENTILATOR ASSOCIATED PNEUMONIA USING MACHINE LEARNING TECHNIQUES: A PROSPECTIVE COHORT STUDY

Projekttitel Englisch

EARLY IDENTIFICATION OF VENTILATOR ASSOCIATED PNEUMONIA USING MACHINE LEARNING TECHNIQUES: A PROSPECTIVE COHORT STUDY

Texte zu diesem Projekt

	Deutsch	Französisch	Italienisch	Englisch
Kurzbeschreibung		-	-
Abstract		-	-

Kategorie	Text
Kurzbeschreibung (Deutsch)	EARLY IDENTIFICATION OF VENTILATOR ASSOCIATED PNEUMONIA USING MACHINE LEARNING TECHNIQUES: A PROSPECTIVE COHORT STUDY
Kurzbeschreibung (Englisch)	EARLY IDENTIFICATION OF VENTILATOR ASSOCIATED PNEUMONIA USING MACHINE LEARNING TECHNIQUES: A PROSPECTIVE COHORT STUDY
Abstract (Deutsch)	VAP is burdened by increased morbidity and mortality. Due to lack of accurate diagnostic criteria we aim to develop different AI-algorithms using data from mechanical ventilators. VAP detection by accurate AI-algorithm has the potential to reduce morbidity, mortality, antibiotic overuse and costs.
Abstract (Englisch)	VAP is burdened by increased morbidity and mortality. Due to lack of accurate diagnostic criteria we aim to develop different AI-algorithms using data from mechanical ventilators. VAP detection by accurate AI-algorithm has the potential to reduce morbidity, mortality, antibiotic overuse and costs.