[Use of neural networks in medicine: concerning dyspeptic pathology].

Abstract:

OBJECTIVES:Development and training of a neurone network that enables the patients who attend the clinic with symptoms of dyspepsia to be classified into two groups: those who very probably have peptic ulcer disease or gastro-oesophageal reflux (GOR) and those more likely to have functional or idiopathic dyspepsia. Results obtained with the neurone network and with other statistical classifiers were compared. DESIGN:Retrospective study. SETTING:Three urban primary care clinics. Participants. 81 patients with a diagnosis of dyspepsia, who underwent a digestive tract endoscopy and/ or oesophageal-gastro-duodenal meal, recorded in the clinical notes. Method. Face-to-face interview with a set questionnaire on the symptoms and risk factors of dyspepsia pathology. Data were analysed with determinist classifier, statistical classifier and neurone network based on a multi-layer perception. RESULTS:The neurone network correctly classified 81% of patients, with negative predictor value of 90% and positive predictor value of 80%. CONCLUSIONS:The neurone network provides very high accuracy rates in classifying patients on the basis of the presence or otherwise of determined symptoms. There was a tendency to distinguish negative diagnoses (functional or idiopathic dyspepsia) better than positive ones (peptic ulcer disease or GOR). Systematic use of neurone networks in primary care clinics would assist the doctor by increasing the accuracy of diagnostic and/or clinical decisions.

journal_name

Aten Primaria

journal_title

Atencion primaria

authors

Sáenz Bajo N,Barrios Rueda E,Conde Gómez M,Domínguez Macías I,López Carabaño A,Méndez Díez C

subject

Has Abstract

pub_date

2002-06-30 00:00:00

pages

99-102

issue

2

eissn

0212-6567

issn

1578-1275

pii

13033735

journal_volume

30

pub_type

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