In this paper we describe an Heart Failure analysis Dashboard that, combined with a handy device for the automatic acquisition of a set of patient's clinical parameters, allows to support telemonitoring functions. The Dashboard's intelligent core is a Computer Decision Support System designed to assist the clinical decision of non-specialist caring personnel, and it is based on three functional parts: Diagnosis, Prognosis, and Follow-up management. Four Artificial Intelligence-based techniques are compared for providing diagnosis function: a Neural Network, a Support Vector Machine, a Classification Tree and a Fuzzy Expert System whose rules are produced by a Genetic Algorithm. State of the art algorithms are used to support a score-based prognosis function. The patient's Follow-up is used to refine the diagnosis.

Guidi, G., M. C., P., Miniati, R., Iadanza, E. (2012). Heart Failure analysis Dashboard for patient's remote monitoring combining multiple artificial intelligence technologies. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp.2210-2213). New York : IEEE [10.1109/EMBC.2012.6346401].

Heart Failure analysis Dashboard for patient's remote monitoring combining multiple artificial intelligence technologies

IADANZA, ERNESTO
2012-01-01

Abstract

In this paper we describe an Heart Failure analysis Dashboard that, combined with a handy device for the automatic acquisition of a set of patient's clinical parameters, allows to support telemonitoring functions. The Dashboard's intelligent core is a Computer Decision Support System designed to assist the clinical decision of non-specialist caring personnel, and it is based on three functional parts: Diagnosis, Prognosis, and Follow-up management. Four Artificial Intelligence-based techniques are compared for providing diagnosis function: a Neural Network, a Support Vector Machine, a Classification Tree and a Fuzzy Expert System whose rules are produced by a Genetic Algorithm. State of the art algorithms are used to support a score-based prognosis function. The patient's Follow-up is used to refine the diagnosis.
978-1-4244-4119-8
978-1-4577-1787-1
Guidi, G., M. C., P., Miniati, R., Iadanza, E. (2012). Heart Failure analysis Dashboard for patient's remote monitoring combining multiple artificial intelligence technologies. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp.2210-2213). New York : IEEE [10.1109/EMBC.2012.6346401].
File in questo prodotto:
File Dimensione Formato  
EMBC12_0668_FI.pdf

non disponibili

Tipologia: Pre-print
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 452.62 kB
Formato Adobe PDF
452.62 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Heart_Failure_analysis_Dashboard_for_patients_remote_monitoring_combining_multiple_artificial_intelligence_technologies.pdf

non disponibili

Tipologia: PDF editoriale
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 482.86 kB
Formato Adobe PDF
482.86 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1215361