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Titolo:
COMPARATIVE-EVALUATION OF THE USE OF ARTIFICIAL NEURAL NETWORKS FOR MODELING THE EPIDEMIOLOGY OF SCHISTOSOMIASIS-MANSONI
Autore:
HAMMAD TA; ABDELWAHAB MF; DECLARIS N; ELSAHLY A; ELKADY N; STRICKLAND GT;
Indirizzi:
MED SERV CORP INT,SCHISTOSOMIASIS RES PROJECT,1761 WILSON BLVD ARLINGTON VA 22209 UNIV MARYLAND,SCH MED,DEPT EPIDEMIOL & PREVENT MED BALTIMORE MD 21201 CAIRO UNIV,SCH MED,DEPT TROP MED CAIRO EGYPT
Titolo Testata:
Transactions of the Royal Society of Tropical Medicine and Hygiene
fascicolo: 4, volume: 90, anno: 1996,
pagine: 372 - 376
SICI:
0035-9203(1996)90:4<372:COTUOA>2.0.ZU;2-G
Fonte:
ISI
Lingua:
ENG
Soggetto:
INFECTIONS;
Keywords:
SCHISTOSOMIASIS; SCHISTOSOMA MANSONI; EPIDEMIOLOGIC MODELING; ARTIFICIAL NEURAL NETWORKS;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
20
Recensione:
Indirizzi per estratti:
Citazione:
T.A. Hammad et al., "COMPARATIVE-EVALUATION OF THE USE OF ARTIFICIAL NEURAL NETWORKS FOR MODELING THE EPIDEMIOLOGY OF SCHISTOSOMIASIS-MANSONI", Transactions of the Royal Society of Tropical Medicine and Hygiene, 90(4), 1996, pp. 372-376

Abstract

There has been a marked increase in the application of approaches based on artificial intelligence (AI) in the field of computer science and medical diagnosis, but AI is still relatively unused in epidemiological settings. In this study we report results of the application of neural networks (NN; a special category of AI) to schistosomiasis. It was possible to design an NN structure which can process and fit epidemiological data collected from 251 schoolchildren in Egypt using the first year's data to predict second and third years' infection rates. Data collected over 3 years included age, gender, exposure to canal waterand agricultural activities, medical history and examination, and stool and urine parasitology. Schistosoma mansoni infection rates were 50%, 78% and 66% at the baseline and the 2 follow-up periods, respectively. NN modelling was based on the standard back-propagation algorithm,in which we built a suitable configuration of the network, using the first year's data, that: optimized performance. It was implemented on an IBM compatible computer using commercially available software. The performance of the NN model in the first year compared favourably withlogistic regression (NN sensitivity=83% (95% confidence interval [CI]78-88%) and positive predictive value (PPV)=63% (95% CI 57-69%); logistic regression sensitivity=66% (95% CI 60%-72%) and PPV=59% (95% CI 53%-65%). The NN model generalized the criteria for predicting infection over time better than logistic regression and showed more stability over time, as it retained its sensitivity and specificity and had better false positive and negative profiles than logistic regression. The potential of NN-based models to analyse and predict wide-scale controlprogramme data, which are inevitably based on unstable egg excretion rates and insensitive laboratory techniques, is promising but still untapped.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 31/03/20 alle ore 10:06:12