Classification of unhealthy and healthy neonates in neonatal intensive care units using medical thermography processing and artificial neural network

2019 ◽  
pp. 1-29
Author(s):  
Duygu Savasci ◽  
Ahmet Haydar Ornek ◽  
Saim Ervural ◽  
Murat Ceylan ◽  
Murat Konak ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Neonatal Intensive Care ◽  
Neonatal Intensive Care Units ◽  
Artificial Neural

scholarly journals An Artificial Neural Network Model for Predicting Successful Extubation in Intensive Care Units

2018 ◽  
Vol 7 (9) ◽  
pp. 240 ◽  
Author(s):  
Meng-Hsuen Hsieh ◽  
Meng-Ju Hsieh ◽  
Chin-Ming Chen ◽  
Chia-Chang Hsieh ◽  
Chien-Ming Chao ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Characteristic Curve ◽  
Chronic Hemodialysis ◽  
Ann Model ◽  
Shallow Breathing ◽  
Artificial Neural ◽  
Successful Extubation

Background: Successful weaning from mechanical ventilation is important for patients in intensive care units (ICUs). The aim was to construct neural networks to predict successful extubation in ventilated patients in ICUs. Methods: Data from 1 December 2009 through 31 December 2011 of 3602 patients with planned extubation in Chi-Mei Medical Center’s ICUs was used to train and test an artificial neural network (ANN). The input was 37 clinical risk factors, and the output was a failed extubation prediction. Results: One hundred eighty-five patients (5.1%) had a failed extubation. Multivariate analyses revealed that failure was positively associated with therapeutic intervention scoring system (TISS) scores (odds ratio [OR]: 1.814; 95% Confidence Interval [CI]: 1.283–2.563), chronic hemodialysis (OR: 12.264; 95% CI: 8.556–17.580), rapid shallow breathing (RSI) (OR: 2.003; 95% CI: 1.378–2.910), and pre-extubation heart rate (OR: 1.705; 95% CI: 1.173–2.480), but negatively associated with pre-extubation PaO2/FiO2 (OR: 0.529; 95%: 0.370–0.750) and maximum expiratory pressure (MEP) (OR: 0.610; 95% CI: 0.413–0.899). A multilayer perceptron ANN model with 19 neurons in a hidden layer was developed. The overall performance of this model was F1: 0.867, precision: 0.939, and recall: 0.822. The area under the receiver operating characteristic curve (AUC) was 0.85, which is better than any one of the following predictors: TISS: 0.58 (95% CI: 0.54–0.62; p < 0.001); 0.58 (95% CI: 0.53–0.62; p < 0.001); and RSI: 0.54 (95% CI: 0.49–0.58; p = 0.097). Conclusions: The ANN performed well when predicting failed extubation, and it will help predict successful planned extubation.

YENİDOĞAN YOĞUN BAKIM ÜNİTELERİNDE İŞ KAZALARI RİSKLERİ VE NEDENLERİNE YÖNELİK BİR ARAŞTIRMA

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Haluk Tanrıverdi ◽  
Orhan Akova ◽  
Nurcan Türkoğlu Latifoğlu
Keyword(s):  
Intensive Care ◽  
Intensive Care Units ◽  
Physical Environment ◽  
Neonatal Intensive Care ◽  
Organizational Factors ◽  
Occupational Accidents ◽  
Neonatal Intensive Care Units ◽  
Work Related ◽  
Physical Conditions ◽  
The Relationship

This study aims to demonstrate the relationship between the qualifications of neonatal intensive care units of hospitals (physical conditions, standard applications, employee qualifications and use of personal protective equipment) and work related causes and risks, employee related causes and risks when occupational accidents occur. Accordingly, a survey was prepared and was made among 105 nurses working in 3 public and 3 private hospital's neonatal intensive care units, in the January of 2010. The survey consists of questions about the qualifications of neonatal intensive care units, work related causes and risks, and employee related causes and risks. From the regression analysis conducted, it has been found that confirmed hypotheses in several studies in the literature were not significant in this study. The sub-dimensions in which relationships has been found show that the improvement of the physical environment in workplace, the improvement of the employee qualifications and standard applications can reduce the rate of occupational accidents. According to the results of this study management should take care of the organizational factors besides to improvement of the physical environment in workplace, the improvement of the employee qualifications and standard applications.

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