A hierarchical Poisson mixture regression model to analyse maternity length of hospital stay

2002 ◽  
Vol 21 (23) ◽  
pp. 3639-3654 ◽  
Author(s):  
K. Wang ◽  
Kelvin K. W. Yau ◽  
Andy H. Lee
Keyword(s):  
Regression Model ◽  
Hospital Stay ◽  
Length Of Hospital Stay ◽  
Poisson Mixture ◽  
Mixture Regression ◽  
Mixture Regression Model

scholarly journals Poisson Mixture Regression Models for Heart Disease Prediction

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Chipo Mufudza ◽  
Hamza Erol
Keyword(s):  
Heart Disease ◽  
Regression Model ◽  
Regression Models ◽  
Information Criteria ◽  
Disease Prediction ◽  
Poisson Mixture ◽  
Mixture Regression ◽  
Concomitant Variable ◽  
Mixture Regression Models ◽  
Mixture Regression Model

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

scholarly journals Plane-extraction from depth-data using a Gaussian mixture regression model

2018 ◽  
Vol 110 ◽  
pp. 44-50 ◽  
Author(s):  
Richard T. Marriott ◽  
Alexander Pashevich ◽  
Radu Horaud
Keyword(s):  
Regression Model ◽  
Gaussian Mixture ◽  
Mixture Regression ◽  
Depth Data ◽  
Plane Extraction ◽  
Mixture Regression Model

Surgical Site Infection in a Tertiary Neonatal Surgery Centre

2018 ◽  
Vol 29 (03) ◽  
pp. 260-265 ◽  
Author(s):  
Adiam Woldemicael ◽  
Sarah Bradley ◽  
Caroline Pardy ◽  
Justin Richards ◽  
Paolo Trerotoli ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Surgical Site Infection ◽  
Hospital Stay ◽  
Logistic Regression Model ◽  
Length Of Hospital Stay ◽  
Neonatal Surgery ◽  
Site Infection ◽  
Increased Risk

Introduction Surgical site infection (SSI) is a key performance indicator to assess the quality of surgical care. Incidence and risk factors for SSI in neonatal surgery are lacking in the literature. Aim To define the incidence of SSI and possible risk factors in a tertiary neonatal surgery centre. Materials and Methods This is a prospective cohort study of all the neonates who underwent abdominal and thoracic surgery between March 2012 and October 2016. The variables analyzed were gender, gestational age, birth weight, age at surgery, preoperative stay in neonatal intensive care unit, type of surgery, length of stay, and microorganisms isolated from the wounds. Statistical analysis was done with chi-square, Student's t- or Mann–Whitney U-tests. A logistic regression model was used to evaluate determinants of risk for SSI; variables were analyzed both with univariate and multivariate models. For the length of hospital stay, a logistic regression model was performed with independent variables. Results A total of 244 neonates underwent 319 surgical procedures. The overall incidence of SSIs was 43/319 (13.5%). The only statistical differences between neonates with and without SSI were preoperative stay (<4 days vs. ≥4 days, p < 0.01) and length of hospital stay (<30 days vs. ≥30 days, p < 0.01). A pre-operative stay longer than 4 days was associated with almost three times increased risk of SSI (odds ratio [OR] 2.96, 95% confidence interval [CI] 1.05–8.34, p = 0.0407). Gastrointestinal procedures were associated with more than ten times the risk of SSI compared with other procedures (OR 10.17, 95% CI 3.82–27.10, p < 0.0001). Gastroschisis closure and necrotizing enterocolitis (NEC) laparotomies had the highest incidence SSI (54% and 62%, respectively). The risk of longer length of hospital stay after SSI was more than three times higher (OR = 3.36, 95%CI 1.63–6.94, p = 0.001). Conclusion This is the first article benchmarking the incidence of SSI in neonatal surgery in the United Kingdom. A preoperative stay ≥4 days and gastrointestinal procedures were independent risk factors for SSI. More research is needed to develop strategies to reduce SSI in selected neonatal procedures.

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