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Titolo:
Knowledge discovery approach to automated cardiac SPECT diagnosis
Autore:
Kurgan, LA; Cios, KJ; Tadeusiewicz, R; Ogiela, M; Goodenday, LS;
Indirizzi:
Univ Colorado, Hlth Sci Ctr, Denver, CO 80217 USA Univ Colorado Denver COUSA 80217 ado, Hlth Sci Ctr, Denver, CO 80217 USA Univ Colorado, Boulder, CO 80309 USA Univ Colorado Boulder CO USA 80309Univ Colorado, Boulder, CO 80309 USA 4cData, LLC, Golden, CO USA 4cData Golden CO USA4cData, LLC, Golden, CO USA Univ Min & Met Krakow, Krakow, Poland Univ Min & Met Krakow Krakow Poland iv Min & Met Krakow, Krakow, Poland Med Coll Ohio, Toledo, OH 43699 USA Med Coll Ohio Toledo OH USA 43699Med Coll Ohio, Toledo, OH 43699 USA
Titolo Testata:
ARTIFICIAL INTELLIGENCE IN MEDICINE
fascicolo: 2, volume: 23, anno: 2001,
pagine: 149 - 169
SICI:
0933-3657(200110)23:2<149:KDATAC>2.0.ZU;2-7
Fonte:
ISI
Lingua:
ENG
Soggetto:
PERFUSION QUANTIFICATION; MYOCARDIAL PERFUSION;
Keywords:
knowledge discovery and data mining; SPECT myocardial perfusion imaging; CLIP3 machine learning algorithm;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Citazioni:
20
Recensione:
Indirizzi per estratti:
Indirizzo: Kurgan, LA Univ Colorado, Hlth Sci Ctr, POB 173364, Denver, CO 80217 USA Univ Colorado POB 173364 Denver CO USA 80217 nver, CO 80217 USA
Citazione:
L.A. Kurgan et al., "Knowledge discovery approach to automated cardiac SPECT diagnosis", ARTIF INT M, 23(2), 2001, pp. 149-169

Abstract

The paper describes a computerized process of myocardial perfusion diagnosis from cardiac single proton emission computed tomography (SPECT) images using data mining and knowledge discovery approach. We use a six-step knowledge discovery process. A database consisting of 267 cleaned patient SPECT images (about 3000 2D images), accompanied by clinical information and physician interpretation was created first. Then, a new user-friendly algorithm for computerizing the diagnostic process was designed and implemented. SPECT images were processed to extract a set of features, and then explicit rules were generated, using inductive machine learning and heuristic approaches to mimic cardiologist's diagnosis. The system is able to provide a set ofcomputer diagnoses for cardiac SPECT studies, and can be used as a diagnostic tool by a cardiologist. The achieved results are encouraging because ofthe high correctness of diagnoses. (C) 2001 Elsevier Science B.V. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 20/06/19 alle ore 17:51:16