Artificial neural networks are able to recognize gastro-oesophageal reflux disease patients solely on the basis of clinical data

European Journal of Gastroenterology and Hepatology

Volumn 17, Issue 6, 2005, Pages 605-610

  Pace, Fabio ^a,e   Buscema, Massimo ^b   Dominici, Patrizia ^c   Intraligi, Marco ^b   Baldi, Fabio ^a   Cestari, Renzo ^d   Passaretti, Sandro ^a   Porro, Gabriele Bianchi ^a   Grossi, Enzo ^c  

^a L Sacco H ^*  (Italy)

^b Semeion centro ricerche di scienze della communicazione   (Italy)

^c Bracco Imaging SpA   (Italy)

^d UNIVERSITY OF BRESCIA   (Italy)

^e UNIVERSITY OF MILAN   (Italy)

Author keywords

Artificial neural networks; Gastro oesophageal reflux disease; Gastro oesophageal reflux disease symptoms; Non gastro oesophageal reflux disease; pH metry

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLINICAL EXAMINATION; DIAGNOSTIC ACCURACY; DISCRIMINANT ANALYSIS; ESOPHAGOSCOPY; ESOPHAGUS PH; FEMALE; GASTROESOPHAGEAL REFLUX; HUMAN; MAJOR CLINICAL STUDY; MALE; PREDICTION; PRIORITY JOURNAL; VALIDATION PROCESS;

ADULT; DIAGNOSIS, COMPUTER-ASSISTED; ESOPHAGOSCOPY; ESOPHAGUS; FEMALE; GASTROESOPHAGEAL REFLUX; HEALTH STATUS INDICATORS; HUMANS; HYDROGEN-ION CONCENTRATION; LIFE STYLE; MALE; MIDDLE AGED; MONITORING, PHYSIOLOGIC; NEURAL NETWORKS (COMPUTER); REPRODUCIBILITY OF RESULTS; SEVERITY OF ILLNESS INDEX;

EID: 19544388763     PISSN: 0954691X     EISSN: None     Source Type: Journal    
DOI: 10.1097/00042737-200506000-00003     Document Type: Article

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