scholarly journals Predicting the serum digoxin concentrations of infants in the neonatal intensive care unit through an artificial neural network

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Shu-Hui Yao ◽  
Hsiang-Te Tsai ◽  
Wen-Lin Lin ◽  
Yu-Chieh Chen ◽  
Chiahung Chou ◽  
...  
Keyword(s):  
Neural Network ◽  
Intensive Care Unit ◽  
Neonatal Intensive Care ◽  
Newborn Infants ◽  
Digoxin Concentration ◽  
Ann Model ◽  
Ann Models ◽  
Clinically Significant ◽  
Toxic Concentrations ◽  
Concentration Prediction

Abstract Background Given its narrow therapeutic range, digoxin’s pharmacokinetic parameters in infants are difficult to predict due to variation in birth weight and gestational age, especially for critically ill newborns. There is limited evidence to support the safety and dosage requirements of digoxin, let alone to predict its concentrations in infants. This study aimed to compare the concentrations of digoxin predicted by traditional regression modeling and artificial neural network (ANN) modeling for newborn infants given digoxin for clinically significant patent ductus arteriosus (PDA). Methods A retrospective chart review was conducted to obtain data on digoxin use for clinically significant PDA in a neonatal intensive care unit. Newborn infants who were given digoxin and had digoxin concentration(s) within the acceptable range were identified as subjects in the training model and validation datasets, accordingly. Their demographics, disease, and medication information, which were potentially associated with heart failure, were used for model training and analysis of digoxin concentration prediction. The models were generated using backward standard multivariable linear regressions (MLRs) and a standard backpropagation algorithm of ANN, respectively. The common goodness-of-fit estimates, receiver operating characteristic curves, and classification of sensitivity and specificity of the toxic concentrations in the validation dataset obtained from MLR or ANN models were compared to identify the final better predictive model. Results Given the weakness of correlations between actual observed digoxin concentrations and pre-specified variables in newborn infants, the performance of all ANN models was better than that of MLR models for digoxin concentration prediction. In particular, the nine-parameter ANN model has better forecasting accuracy and differentiation ability for toxic concentrations. Conclusion The nine-parameter ANN model is the best alternative than the other models to predict serum digoxin concentrations whenever therapeutic drug monitoring is not available. Further cross-validations using diverse samples from different hospitals for newborn infants are needed.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

BACTERIAL COLONIZATION OF NEWBORN INFANTS IN A NEONATAL INTENSIVE CARE UNIT

1982 ◽  
Vol 71 (5) ◽  
pp. 779-783 ◽  
Author(s):  
M. ERIKSSON ◽  
B. MELÉN ◽  
K.-E. MYRBÄCK ◽  
B. WINBLADH ◽  
R. ZETTERSTRÖM
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Neonatal Intensive Care Unit ◽  
Neonatal Intensive Care ◽  
Bacterial Colonization ◽  
Newborn Infants

Staphylococcus epidermidis Bacteremia in Neonates: Further Observations and the Occurrence of Focal Infection

PEDIATRICS ◽  
1984 ◽  
Vol 74 (5) ◽  
pp. 832-837 ◽  
Author(s):  
Gary J. Noel ◽  
Paul J. Edelson
Keyword(s):  
Intensive Care Unit ◽  
New York ◽  
Intensive Care ◽  
Neonatal Intensive Care Unit ◽  
Staphylococcus Epidermidis ◽  
Neonatal Intensive Care ◽  
Blood Cultures ◽  
Focal Infection ◽  
Clinically Significant ◽  
Vascular Catheters

The frequency and clinical significance of Staphylococcus epidermidis isolates from blood cultures of neonates collected during a 17-month period in The New York Hospital neonatal intensive care unit (NICU) were reviewed. Twenty-three episodes of clinically significant S epidermidis bacteremia were detected using the criteria of isolation from 3/3 blood culture bottles from a single culture, or isolation from two or more blood cultures taken at different times, or simultaneous isolation from blood and fluid, pus or vascular catheter. Of these 23 episodes of S epidermidis bacteremia, ten were associated with colonized vascular catheters, and four episodes occurred in infants with necrotizing enterocolitis. Focal S epidermidis infection occurred in ten episodes, and persistent bacteremia occurred frequently in this setting. S epidermidis was the most frequent cause of bacteremia in the Neonatal Intensive Care Unit during the period reviewed. Of the isolates determined to be clinically significant, 74% were resistant to methicillin and cephalothin and 91% were resistant to gentamicin. All isolates were sensitive to vancomycin. In addition to removing vascular catheters suspected of being colonized and searching for potential sites of focal infection, an antibiotic regimen that includes vancomycin should be initiated once significant S epidermidis bacteremia has been recognized in the neonate.

scholarly journals Human Herpesvirus 6 Meningitis in a Neonatal Case

2021 ◽  
Vol 28 (4) ◽  
pp. 153-156
Author(s):  
Gyu Min Yeon ◽  
Yu Jin Jung
Keyword(s):  
Intensive Care Unit ◽  
Neonatal Intensive Care ◽  
Neonatal Period ◽  
Newborn Infants ◽  
Human Herpesvirus ◽  
Human Herpesvirus 6 ◽  
Chain Reaction ◽  
Polymerase Chain ◽  
Herpesvirus 6 ◽  
Infancy And Early Childhood

Incidence of human herpesvirus-6 (HHV-6) infection in the neonatal period has been reported in few cases. HHV-6, commonly responsible for roseola, is known to establish infection during infancy and early childhood. A 14-day-old neonate, presented with a fever of 38.3℃, primarily due to an HHV-6 infection, was admitted to our neonatal intensive care unit. A polymerase chain reaction (PCR) of his cerebrospinal fluid was positive for HHV-6. Additionally, serology for HHV-6 PCR was positive. We believe that HHV-6 can cause infection in febrile newborn infants.

Application of artificial neural network to control the coagulant dosing in water treatment plant

2000 ◽  
Vol 42 (3-4) ◽  
pp. 403-408 ◽  
Author(s):  
R.-F. Yu ◽  
S.-F. Kang ◽  
S.-L. Liaw ◽  
M.-c. Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Water Treatment ◽  
Regression Model ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Ann Model ◽  
Coagulant Dosage ◽  
Ann Models

Coagulant dosing is one of the major operation costs in water treatment plant, and conventional control of this process for most plants is generally determined by the jar test. However, this method can only provide periodic information and is difficult to apply to automatic control. This paper presents the feasibility of applying artificial neural network (ANN) to automatically control the coagulant dosing in water treatment plant. Five on-line monitoring variables including turbidity (NTUin), pH (pHin) and conductivity (Conin) in raw water, effluent turbidity (NTUout) of settling tank, and alum dosage (Dos) were used to build the coagulant dosing prediction model. Three methods including regression model, time series model and ANN models were used to predict alum dosage. According to the result of this study, the regression model performed a poor prediction on coagulant dosage. Both time-series and ANN models performed precise prediction results of dosage. The ANN model with ahead coagulant dosage performed the best prediction of alum dosage with a R2 of 0.97 (RMS=0.016), very low average predicted error of 0.75 mg/L of alum were also found in the ANN model. Consequently, the application of ANN model to control the coagulant dosing is feasible in water treatment.

Shiva’s Babies

2019 ◽  
pp. 124-141
Author(s):  
Swasti Bhattacharyya
Keyword(s):  
United States ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
High Risk ◽  
Neonatal Intensive Care ◽  
Newborn Infants ◽  
Nationwide Survey ◽  
The United States ◽  
Cultural Humility ◽  
Religious Perspectives

Discussing religious views from within any tradition is challenging because they are not monolithic. However, it is worth exploring religious perspectives because they are often the foundation, whether conscious or not, of the reasoning underlying people’s decisions. Following a brief discussion on the importance of cultural humility and understanding the worldview of patients, the author focuses on Hindu perspectives regarding the care of infants in the neonatal intensive care unit. Along with applying six elements of Hindu thought (underlying unity of all life, multivalent nature of Hindu traditions, dharma, emphasis on societal good, karma, and ahimsa), the author incorporates perspectives of Hindu adults, living in the United States, who responded to a nationwide survey regarding the care of high-risk newborn infants in the hospital.

Development of Neural Network Models for a Crude Oil Distillation Column

2012 ◽  
Author(s):  
Khairiyah Mohd. Yusof ◽  
Fakhri Karray ◽  
Peter L. Douglas
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Crude Oil ◽  
Distillation Column ◽  
Network Models ◽  
Ann Model ◽  
Neural Network Models ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Crude Oil Distillation

This paper discusses the development of artificial neural network (ANN) models for a crude oil distillation column. Since the model is developed for real time optimisation (RTO) applications they are steady state, multivariable models. Training and testing data used to develop the models were generated from a reconciled steady-state model simulated in a process simulator. The radial basis function networks (RBFN), a type of feedforward ANN model, were able to model the crude tower very well, with the root mean square error for the prediction of each variable less than 1%. Grouping related output variables in a network model was found to give better predictions than lumping all the variables in a single model; this also allowed the overall complex, multivariable model to be simplified into smaller models that are more manageable. In addition, the RBFN models were also able to satisfactorily perform range and dimensional extrapolation, which is necessary for models that are used in RTO.

Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests

2022 ◽  
pp. 1287-1300
Author(s):  
Balaji Prabhu B. V. ◽  
M. Dakshayini
Keyword(s):  
Neural Network ◽  
Crop Production ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Ann Model ◽  
Demand Prediction ◽  
Computational Performance ◽  
Societal Needs ◽  
Ann Models ◽  
Artificial Neural Network Ann

Demand forecasting plays an important role in the field of agriculture, where a farmer can plan for the crop production according to the demand in future and make a profitable crop business. There exist a various statistical and machine learning methods for forecasting the demand, selecting the best forecasting model is desirable. In this work, a multiple linear regression (MLR) and an artificial neural network (ANN) model have been implemented for forecasting an optimum societal demand for various food crops that are commonly used in day to day life. The models are implemented using R toll, linear model and neuralnet packages for training and optimization of the MLR and ANN models. Then, the results obtained by the ANN were compared with the results obtained with MLR models. The results obtained indicated that the designed models are useful, reliable, and quite an effective tool for optimizing the effects of demand prediction in controlling the supply of food harvests to match the societal needs satisfactorily.

Liberal versus Indicated Maternal Oxygen Supplementation in Labor: A Before-and-After Trial

2018 ◽  
Vol 35 (11) ◽  
pp. 1057-1064 ◽  
Author(s):  
Sean Blackwell ◽  
Mesk Alrais ◽  
Farah Amro ◽  
Rachel Wiley ◽  
Patricia Heale ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Neonatal Intensive Care ◽  
Umbilical Artery ◽  
Supplemental Oxygen ◽  
Oxygen Supplementation ◽  
Before And After ◽  
Clinically Significant ◽  
Nicu Admission ◽  
Singleton Pregnancies

Background Although supplemental oxygen (SO2) is routinely administered to laboring gravidas, benefits and harms are not well studied. Objective This article compares strategies of liberal versus indicated SO2 therapy during labor on cesarean delivery (CD) rate and neonatal outcomes. Study Design A controlled, before-and-after trial of laboring women with term, singleton pregnancies. During an initial 8-week period, maternal SO2 was administered at the discretion of the provider followed by an 8-week period where SO2 was to be given only for protocol indications. Results Our study included 844 women. There was no difference in number of women receiving SO2 (53% liberal vs. 50% indicated; p = 0.33). For those receiving SO2, there was no difference in SO2 duration (median, 89 minutes [interquartile range, 42–172] vs. 87 minutes [36–152]; p = 0.42). There were no differences in overall CD rate (20% vs. 17%; p = 0.70), CD for nonreassuring fetal status, or use of intrauterine resuscitative measures. There were more 5-minute APGAR < 7 in the indicated group, but no difference in umbilical artery pH < 7.1 or neonatal intensive care unit (NICU) admission. Conclusion Approximately half of women receive SO2 intrapartum regardless of a strategy of liberal or indicated oxygen use. There were no clinically significant differences in outcomes between strategies.

scholarly journals Continued Enteral Feeding Is Beneficial in Hypoglycemic Infants Admitted to Intensive Care for Parenteral Dextrose Therapy

2019 ◽  
Vol 6 ◽  
pp. 2333794X1985741
Author(s):  
Mahdi Alsaleem ◽  
Lina Saadeh ◽  
Vasantha H. S. Kumar ◽  
Gregory E. Wilding ◽  
Lorin Miller ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Neonatal Intensive Care ◽  
Newborn Infants ◽  
Glucose Infusion ◽  
Infusion Therapy ◽  
Care Providers ◽  
Neonatal Hypoglycemia ◽  
Lower Maximum ◽  
Length Of Hospitalization

There is variability in practice among care providers on feeding infants admitted with neonatal hypoglycemia (NH) for parenteral dextrose. We compared clinical outcomes in infants who were fed (NH-Fed) and hypoglycemic infants who were kept nothing per os (NPO) (NH-NPO) at the time of initiation of intravenous (IV) dextrose. We performed a retrospective review of all newborn infants admitted to the neonatal intensive care unit with NH for IV dextrose. Infants were grouped as per the feeding approach at initiation of IV dextrose: NH-Fed or NH-NPO infants. We found that infants in the NH-Fed group had lower maximum glucose infusion rate, less duration of glucose infusion therapy compared with the NH-NPO group, and significantly less number of days of hospital stay compared with the NH-NPO group (5.87 ± 1.4 days vs 4.9 ± 1.4 days, P < .006). In conclusion, feeding infants with hypoglycemia who require IV dextrose offers tangible benefits of shorter duration of parenteral dextrose and shorter length of hospitalization.

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