scholarly journals Clusterwise Regression Model Development with Gamma Distribution

2020 ◽  
Author(s):  
Reski Syafruddin ◽  
Agus M. Soleh ◽  
Aji H. Wigena
Keyword(s):  
Regression Model ◽  
Gamma Distribution ◽  
Model Development ◽  
Clusterwise Regression

scholarly journals Machine Learning for Mortality Prediction in Pediatric Myocarditis

2021 ◽  
Vol 9 ◽  
Author(s):  
Fu-Sheng Chou ◽  
Laxmi V. Ghimire
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Regression Model ◽  
Current Knowledge ◽  
External Validation ◽  
Model Development ◽  
Kidney Injury ◽  
High Specificity ◽  
Mortality Prediction ◽  
Linear Regression Models

Background: Pediatric myocarditis is a rare disease. The etiologies are multiple. Mortality associated with the disease is 5–8%. Prognostic factors were identified with the use of national hospitalization databases. Applying these identified risk factors for mortality prediction has not been reported.Methods: We used the Kids' Inpatient Database for this project. We manually curated fourteen variables as predictors of mortality based on the current knowledge of the disease, and compared performance of mortality prediction between linear regression models and a machine learning (ML) model. For ML, the random forest algorithm was chosen because of the categorical nature of the variables. Based on variable importance scores, a reduced model was also developed for comparison.Results: We identified 4,144 patients from the database for randomization into the primary (for model development) and testing (for external validation) datasets. We found that the conventional logistic regression model had low sensitivity (~50%) despite high specificity (>95%) or overall accuracy. On the other hand, the ML model struck a good balance between sensitivity (89.9%) and specificity (85.8%). The reduced ML model with top five variables (mechanical ventilation, cardiac arrest, ECMO, acute kidney injury, ventricular fibrillation) were sufficient to approximate the prediction performance of the full model.Conclusions: The ML algorithm performs superiorly when compared to the linear regression model for mortality prediction in pediatric myocarditis in this retrospective dataset. Prospective studies are warranted to further validate the applicability of our model in clinical settings.

Investigating the correlation of COVID-19 spread with Baidu search data : Infoveillance Study (Preprint)

2021 ◽  
Author(s):  
Xiao Qi ◽  
Su-Zhen WANG ◽  
Jia-Ning Feng ◽  
Gao-Pei ZHu ◽  
Yu-Jie Liu ◽  
...  
Keyword(s):  
Public Health ◽  
Regression Model ◽  
Health System ◽  
Early Warning ◽  
Public Health System ◽  
Model Development ◽  
Real Time Analysis ◽  
Early Warning Mechanism ◽  
Search Data ◽  
Search Index

BACKGROUND The sudden outbreak of COVID-19 has placed an unprecedented pressure on China's public health system. It is imperative to strengthen the capacity of early surveillance and early warning to build a sound public health system. Therefore, it is necessary to improve the multi-channel monitoring and early warning mechanism to improve the ability of real-time analysis and judgment. OBJECTIVE To explore the correlation of COVID-19 spread with Baidu search data in Beijing, so as to evaluate the possibility of monitoring the epidemic situation of COVID-19 with Baidu search data. METHODS This study compared the daily case counts of COVID-19 outbreak from January 20 to March 1, 2020 with Baidu search data for the same period in Beijing. After keyword selection, filtering and composition, the most correlated lag of the COVID-19 Baidu Search Index (CBSI) was used for comparison and linear regression model development. RESULTS Our findings showed a positive relationship of CBSI and the confirmed cases of COVID-19 (ρ=0.711, P < .001). The strongest correlation between COVID-19 confirmed cases and indices, CBSI, was at a lag of -11 days. The regression coefficient β1 of the established regression model was equal to 1.042 (P<.001), R2 was equal to 0.7, which indicated that Baidu search data could reflect 70% of the variation in COVID-19 cases. CONCLUSIONS COVID-19 Baidu Search index may be a good monitoring indicator for early detection of COVID-19 outbreaks.

Next-Day Daily Energy Consumption Forecast Model Development and Model Implementation

2012 ◽  
Vol 134 (3) ◽  
Author(s):  
Li Song ◽  
Ik-seong Joo ◽  
Subroto Gunawan
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Model Development ◽  
Thermal Storage ◽  
Forecast Model ◽  
Automation System ◽  
Peak Demand ◽  
Critical Set ◽  
Complete Control

Thermal storage systems were originally designed to shift on-peak cooling production to off-peak cooling production in order to reduce on-peak electricity demand. Recently, however, the reduction of both on- and off-peak demand is a critical issue. Reduction of on- and off-peak demand can also extend the life span and defer or eliminate the replacement of power transformers. Next day electricity consumption is a critical set point to operate chillers and associated pumps at the appropriate time. In this paper, a data evaluation process using the annual daily average cooling consumption of a building was conducted. Three real-time building load forecasting models were investigated: a first-order autoregressive model (AR(1)), an autogressive integrated moving average model (ARIMA(0,1,0)), and a linear regression model. A comparison of results shows that the AR(1) and ARIMA(0,1,0) models provide superior results to the linear regression model, except that the AR(1) model has a few unacceptable spikes. A complete control algorithm integrated with a corrected AR(1) forecast model for a chiller plant including chillers, thermal storage system, and pumping systems was developed and implemented to verify the feasibility of applying this algorithm in the building automation system. Application results are also introduced in the paper.

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