Improvement and automation of artificial neural networks to estimate medical outcomes

Medical Engineering and Physics

Volumn 26, Issue 4, 2004, Pages 321-328

  Ennett, Colleen M ^a   Frize, Monique ^a,b   Charette, Elaine ^b  

^a CARLETON UNIVERSITY   (Canada)

^b UNIVERSITY OF OTTAWA   (Canada)

Author keywords

Artificial neural networks; Automation; Optimization; Outcome prediction; Sensitivity; Stopping criterion

Indexed keywords

AUTOMATION; DATABASE SYSTEMS; ESTIMATION; INTENSIVE CARE UNITS; OPTIMIZATION; SURGERY;

AUTOMTAED SYSTEMS; MEDICAL DATABASES;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; CORONARY ARTERY SURGERY; DATA BASE; INTENSIVE CARE UNIT; MATHEMATICAL ANALYSIS; NEWBORN INTENSIVE CARE; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY;

CLUSTER ANALYSIS; DATABASES, FACTUAL; DIAGNOSIS, COMPUTER-ASSISTED; EXPERT SYSTEMS; HEART DISEASES; HUMANS; INFANT, NEWBORN; INFANT, NEWBORN, DISEASES; INTENSIVE CARE; NEURAL NETWORKS (COMPUTER); OUTCOME ASSESSMENT (HEALTH CARE); PATTERN RECOGNITION, AUTOMATED; PROGNOSIS; QUALITY CONTROL; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY; TREATMENT OUTCOME;

EID: 2342599129     PISSN: 13504533     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.medengphy.2003.09.005     Document Type: Article

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