The Use of Artificial Neural Networks to Determine In-Hospital Mortality After Coronary Artery Bypass Surgery.

OBJECTIVES: The aim of this study was to present an artificial neural network (ANN) model for the accurate estimation of in-hospital mortality and to demonstrate the validity of the model with real data and a comparison with conventional multiple linear regression models. DESIGN: Retrospective clinical study. SETTING: University hospital. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Data were collected from the medical records of 88 patients who had undergone coronary artery bypass graft surgery with an extracorporeal cardiopulmonary pump between January 2018 and March 2020. An ANN approach was used to assess the association between in-hospital mortality and variables from preoperative, intraoperative, and postoperative data garnered retrospectively from patient files. The study examined the data of 88 patients with a mean age of 62.4 ± ten years, 60 (68.1%) of whom were men and 28 (31.8%) of whom were women. An examination of the average success of the training algorithms in the training, validation, and test sets revealed that the quick propagation algorithm ranked first with 97.397%. The algorithm that best matched the present study's dataset was the batch back propagation algorithm, with an average of 99.622 (in other words, this training set accurately estimated 99.622% of every 100 items of data). Furthermore, the rates continuously were greater than 90% when the probability of estimating the estimated output was examined. CONCLUSION: The ANN model tended to outperform multiple linear regression models in predicting in-hospital mortality among patients who have undergone coronary artery bypass graft surgery. Physicians can make use of this information as an aid in performing treatments and ensuring that more accurate quality of surgical care is achieved.

Comparing the alternatives for the most favourable personal protective equipment.

Evaluation of personal protective equipment is a very demanding task in designing an effective workplace safety programme. There is variable equipment to prevent job accidents, to protect workers' health and safety and to minimize the damage of any possible accident. Comparing the alternatives for the most favourable equipment, e.g. possible high noise levels, is one of the most difficult issues to deal with. In this study, the analytic hierarchy process method allows selection of personal protective equipment analytically, which is used to decide on efficient personal protection equipment when choosing protective shoes, helmets, earmuffs and dust masks.

A neural network-based algorithm for predicting the spontaneous passage of ureteral stones.

In this study, a prototype artificial neural network model (ANN) was used to estimate the stone passage rate and to determine the effectivity of predictive factors on this rate in patients with ureteral stones. The retrospective study included a total of 192 patients with ureteral stones, comprising 128 (66.7%) men and 64 (33.3%) women. Patients were divided into two groups. Group 1 (n: 125) consisted of people who spontaneously passed their stones, Group 2 (n: 67) consisted of people who could not pass stones spontaneously. The groups were compared with regard to the relationship between input data and stone passage rate by using both ANN and standard statistical tests. To implement the ANN, the patients were randomly divided into three groups: (a) training group (n = 132), (b) validation group (n = 30), and (c) test group (n = 30). The accuracy rate of ANN in the estimation of the stone passage ratio was 99.1% in the group a, 89.9% in the group b, and 87.3% in the group c. It was revealed that certain criteria (stone size, body weight, pain score, ESR, and CRP) were relatively more significant for saving treatment cost and time and for avoiding unnecessary treatment. ANN can be highly useful for the avoidance of unnecessary interventions in patients with ureteral stones as it showed remarkably high performance in the estimation of stone passage rate (99.16%).

Use of artificial neural networks in the management of antenatally diagnosed ureteropelvic junction obstruction.

BACKGROUND: In this study, an artificial neural network (ANN) based system has been developed specifically to help in the management of antenatally diagnosed uretero-pelvic junction (UPJ) obstruction. METHODS: A total of 53 infants with antenatally detected hydronephrosis caused by UPJ obstruction were included in this study. A neural network was developed with the help of a commercially available software package. The patients' age and sex, renal pelvic diameter, laterality, split renal function and presence of renal scar on radionuclide scan, follow-up times, urine culture results and the presence of symptomatic infections were used as variables. These data were also entered into a statistical software package and linear regression analysis was done. RESULTS: During the follow-up period, 36 children were observed, and the remaining 17 renal units underwent pyeloplasty. The average sensitivity of the ANN model in predicting the outcome was found to be 92% in the training group and 75% in the validation and test groups. In linear regression, none of the predictors were found to be statistically significant. INTERPRETATION: In this study, we have demonstrated that the use of ANNs in antenatally diagnosed UPJ obstruction can help the clinician in making treatment decisions, and thus can be useful in daily clinical practice.

The use of artificial neural networks in decision support in vesicoureteral reflux treatment.

AIM: To develop a prediction model based on artificial neural networks (ANN) for the treatment selection in vesicoureteral reflux (VUR). METHODS: A total of 96 children with VUR (145 ureteric units (UU)) were treated at our institution during 2004-2006. An ANN based on quick propagation architecture was created with the commercially available software package. The patients' age and sex, the cause and grade of VUR, the affected ureter, the type of treatment (conservative, subureteric injection, or open surgery), existence of renal scar on DMSA, follow-up times and the number of injections were used as variables. These data were also transferred to a statistical software package and regression analysis was done. RESULTS: In all, 105 UU showed no reflux, 5 UU showed improvements in reflux grade (considered only in the conservative management group), and the remaining 35 UU showed persistence. In the training group (n = 99), ANN showed 98.5% sensitivity, 92.5% specificity, 97% positive predictive value, and 96% negative predictive value in predicting treatment outcome. CONCLUSIONS: We have demonstrated that ANN can accurately predict the resolution of VUR, and thus could be useful in daily clinical practice. This approach would allow urologists to aid in the decision-making process of VUR treatment.