To improve the access to medical information is necessary to design and implement integrated informatics techniques aimed to gather data from different and heterogeneous sources. This paper describes the technologies used to integrate data coming from the electronic medical record of the IRCCS Fondazione Maugeri (FSM) hospital of Pavia, Italy, and combines them with administrative, pharmacy drugs purchase coming from the local healthcare agency (ASL) of the Pavia area and environmental open data of the same region. The integration process is focused on data coming from a cohort of one thousand patients diagnosed with Type 2 Diabetes Mellitus (T2DM). Data analysis and temporal data mining techniques have been integrated to enhance the initial dataset allowing the possibility to stratify patients using further information coming from the mined data like behavioral patterns of prescription-related drug purchases and other frequent clinical temporal patterns, through the use of an intuitive dashboard controlled system.

Data Integration Technologies to Improve Clinical Decisions on T2DM Patients

Segagni, D;Sacchi, L;Dagliati, A;Tibollo, V;Leporati, P;De Cata, P;Chiovato, L;Bellazzi, R
2015-01-01

Abstract

To improve the access to medical information is necessary to design and implement integrated informatics techniques aimed to gather data from different and heterogeneous sources. This paper describes the technologies used to integrate data coming from the electronic medical record of the IRCCS Fondazione Maugeri (FSM) hospital of Pavia, Italy, and combines them with administrative, pharmacy drugs purchase coming from the local healthcare agency (ASL) of the Pavia area and environmental open data of the same region. The integration process is focused on data coming from a cohort of one thousand patients diagnosed with Type 2 Diabetes Mellitus (T2DM). Data analysis and temporal data mining techniques have been integrated to enhance the initial dataset allowing the possibility to stratify patients using further information coming from the mined data like behavioral patterns of prescription-related drug purchases and other frequent clinical temporal patterns, through the use of an intuitive dashboard controlled system.
2015
978-1-4244-9271-8
File in questo prodotto:
Non ci sono file associati a questo prodotto.

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/11571/1349383
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact