Heart failure patients have become an important challenge for the healthcare system, since they represent a medical, social and economic problem. Early heart failure diagnoses can be very useful to improve patients' quality of life and to reduce the resources consumption, but they can be complex for the general practitioners. Data mining and machine learning techniques can really help in this field. The aim of this study is to validate some machine learning models to identify heart failure patients, starting from administrative data, and to make them transparent and interpretable. Despite the lack of clinical data, not available in Italy, but the most employed for the identification of heart failure patients, the results are comparable with the state-of-the-art ones and the models outperform the performances already obtained in Tuscany.

Exploring machine learning algorithms to identify heart failure patients: The tuscany region case study

Panicacci S.;Donati M.;Fanucci L.;
2019-01-01

Abstract

Heart failure patients have become an important challenge for the healthcare system, since they represent a medical, social and economic problem. Early heart failure diagnoses can be very useful to improve patients' quality of life and to reduce the resources consumption, but they can be complex for the general practitioners. Data mining and machine learning techniques can really help in this field. The aim of this study is to validate some machine learning models to identify heart failure patients, starting from administrative data, and to make them transparent and interpretable. Despite the lack of clinical data, not available in Italy, but the most employed for the identification of heart failure patients, the results are comparable with the state-of-the-art ones and the models outperform the performances already obtained in Tuscany.
2019
978-1-7281-2286-1
File in questo prodotto:
File Dimensione Formato  
editoriale.pdf

solo utenti autorizzati

Tipologia: Versione finale editoriale
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 141.6 kB
Formato Adobe PDF
141.6 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
postprint.pdf

accesso aperto

Tipologia: Documento in Post-print
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 187.92 kB
Formato Adobe PDF
187.92 kB Adobe PDF Visualizza/Apri

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/11568/1019418
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 2
social impact