NDLI: Early detection of decompensation conditions in heart failure patients by knowledge discovery: The HEARTFAID approaches

Content Provider	IEEE Xplore Digital Library
Author	Candelieri, A. Conforti, D. Perticone, F. Sciacqua, A. Kawecka-Jaszcz, K. Styczkiewicz, K.
Copyright Year	2008
Description	Author affiliation: Lab. of Decision Eng. for Health Care Delivery, Univ. of Calabria, Rende (Candelieri, A.; Conforti, D.) \|\| I Cardiac Dept., Jegiellonian Univ. of Krakow, Krakow (Kawecka-Jaszcz, K.; Styczkiewicz, K.) \|\| Cardiovascular Diseases Div., Univ. "Magna Graecia" of Catanzaro, Catanzaro (Perticone, F.; Sciacqua, A.)
Abstract	For the clinical management of chronic heart failure (CHF) patient, a crucial mid-long term goal is the early detection of new acute decompensation events, for improving quality of outcomes while reducing costs on the healthcare system. Within the relevant clinical protocols and guidelines, a general consensus has not been reached about the prediction of further decompensations, even though many different evidence- based indications are known. Under this respect we adopted Knowledge Discovery (KD) approaches as a practical and effective solution to extract new potentially useful facts from repositories of pertinent clinical data. In fact, we present the KD task which has been implemented into the EU FP6 Project HEARTFAID (www.heartfaid.org), aiming at developing an innovative knowledge based platform of services for more effective and efficient clinical management of heart failure within elderly population. 49 CHF patients were recurrently visited by cardiologist, measuring clinical parameters taken from clinical guidelines and evidence- based knowledge. Also general information about each patient was taken into account for the analysis. Several KD algorithms were applied on collected data, obtaining different binary classifiers performing the early detection of new acute decompensation events. Some of these models are "easy-to-understand" and their consistency was directly evaluated by the cardiologists. Moreover, high percentage of correct classifications (above 87%) was obtained by using suitable validation approaches.
Starting Page	893
Ending Page	896
File Size	297196
Page Count	4
File Format	PDF
ISBN	9781424437061
ISSN	02766547
DOI	10.1109/CIC.2008.4749186
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-14
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Computers in Cardiology(CinC)
Subject Keyword	Heart Event detection Guidelines Cardiology Quality management Costs Medical services Protocols Data mining Innovation management
Content Type	Text
Resource Type	Article

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Knowledge Discovery Approaches for Early Detection of Decompensation Conditions in Heart Failure Patients

Biomedical data acquisition and processing in the Decision Support services of HEARTFAID platform

The Innovation of Manufacturing Premise Based on Knowledge Obtained by Quality Accident Report

Software engineer’s approach towards cardiac knowledge discovery

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Introduction of a telemonitoring service for patients affected by Chronic Heart Failure

An informative system for chronic heart failure patients follow-up (CHeF)

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Early detection of decompensation conditions in heart failure patients by knowledge discovery: The HEARTFAID approaches

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Knowledge Discovery Approaches for Early Detection of Decompensation Conditions in Heart Failure Patients

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