Application of combined genetic algorithms with cascade correlation to diagnosis of delayed gastric emptying from electrogastrograms

Comparison of artificial neural networks an support vector machines for feature selection in electrogastrography signal processing

The paper describes a feature selection process applied to electrogastrogram (EGG) processing. The data set is formed by 42 EGG records from functional dyspeptic (FD) patients and 22 from healthy controls. A wrapper configuration classifier was implemented to discriminate between both classes. The aim of this work is to compare artificial neural networks (ANN) and support vector machines (SVM) when acting as fitness functions of a genetic algorithm (GA) that performs a feature selection process over some features extracted from the EGG signals. These features correspond to those that literature shows to be the most used in EGG analysis. The results show that the SVM classifier is faster, requires less memory and reached the same performance (86% of exactitude) than the ANN classifier when acting as the fitness function for the GA.

Effects of a prethickened formula on esophageal pH and gastric emptying of infants with GER

OBJECTIVE(S)

Advantages of prethickened formulas (AR) with regards to esophageal pH and gastric emptying were investigated in this study as compared with a regular formula (R).

STUDY DESIGN

Seventy-four healthy infants, <6 months old, with gastroesophageal reflux were enrolled into the study. All infants underwent 24-hour esophageal pH monitoring while receiving R and AR, alternately. Electrogastrography was measured before and after feeding each study formula. Thereafter, the infants were randomly assigned to receive either R or AR for 1 month. Episodes of regurgitation, vomiting, coughing, crying, and bowel movements were recorded on a weekly interval.

RESULTS

Reflux index (RI) of AR-fed infants were lower (5.64%) than the R-fed infants (7.77%) showing a significant difference (P<0.01) between the 2 groups, favoring AR. Eighty-seven percent of infants improved their RI while receiving the AR formula. Electrogastrography variables were not significantly different between the 2 study groups. A significant decrease (P<0.01) in the daily episodes of regurgitation and vomiting was observed in the AR-fed infants. No adverse events were reported during the study.

CONCLUSIONS

Prethickened infant formula was effective in reducing clinical symptoms of uncomplicated gastroesophageal reflux and reducing RI. Prethickened formulas do not alter gastric emptying time and was very well tolerated.

A review of evidence of health benefit from artificial neural networks in medical intervention

The purpose of this review is to assess the evidence of healthcare benefits involving the application of artificial neural networks to the clinical functions of diagnosis, prognosis and survival analysis, in the medical domains of oncology, critical care and cardiovascular medicine. The primary source of publications is PUBMED listings under Randomised Controlled Trials and Clinical Trials. The rĵle of neural networks is introduced within the context of advances in medical decision support arising from parallel developments in statistics and artificial intelligence. This is followed by a survey of published Randomised Controlled Trials and Clinical Trials, leading to recommendations for good practice in the design and evaluation of neural networks for use in medical intervention.