Appropriate Use of the Glasgow Coma Scale in Intubated Patients
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The Glasgow Coma Scale (GCS) has been shown to be a valuable tool in assessing the neurologic and physiologic status of critically ill patients. Unfortunately, the GCS requires assessment of the verbal response of the patient and this can be blocked by intubation. The purpose of this study was to assess the ability of a regression model based upon the eye and motor components of the GCS to accurately predict the verbal response of the GCS. The primary hypothesis was that the verbal response could be derived from the motor and eye responses of the GCS.Data were collected prospectively in an intensive care unit computer data base. Patients were divided into training and test data sets. Linear regression was used to derive a model of verbal score from the motor and eye scores of the GCS in the training data set. Correlation between the actual and the predicted verbal scores was calculated.A total of 2,521 GCS assessments were available for analysis. The second order multiple regression model was an accurate predictor of the verbal score (Pearson's Correlation r = 0.9, R2 = 0.8, p = 0.0001) in 1,463 observations in the training data set. Second Order Multiple Regression Model: Estimated GCS Verbal = (2.3976) + [GCS Motor x (-0.9253)] + [GCS Eye x (-0.9214)] + [(GCS Motor)2 x (0.2208)] + [(GCS Eye)2 x (0.2318)] where r = 0.91, R2 = 0.83, and p = 0.0001. The accuracy of this model was confirmed by comparing the predicted verbal score to the actual verbal score in the test data set (n = 736, r = 0.92, R2 = 0.85, p = 0.0001)The GCS is a useful tool in the intensive care unit and a critical part of the APACHE II assessment of patient acuity. GCS has been shown to be a useful tool in its own right as a predictor of outcome in the critically ill. Its use is limited with intubation. (See Segatore M, Way C: Heart Lung 21:548, 1992; and Lieh-Lai MW, Theodorou AA, Sarnaik AP, et al: J Pediatr 120:195, 1992.) The present study demonstrates that a relatively simple regression model can use the eye and motor components of the GCS to predict the expected verbal component of the GCS, thus allowing the calculation of the GCS sum score in intubated patients.Path coefficient
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Heart rate variability (HRV) is an effective tool for objectively evaluating physiological stress indices in psychological states. This study aimed to develop multiple linear regression equations to predict HRV variables using physical characteristics, body composition, and heart rate (HR) variables (eg, sex, age, height, weight, body mass index, fat-free mass, percent body fat, resting HR, maximal HR, and HR reserve) in Korean adults. Six hundred eighty adults (male, n = 236, female, n = 444) participated in this study. HRV variable estimation multiple linear regression equations were developed using a stepwise technique. The regression equation’s coefficient of determination for time-domain variables was significantly high (SDNN = adjusted R 2 : 73.6%, P < .001; RMSSD = adjusted R 2 : 84.0%, P < .001; NN50 = adjusted R 2 : 98.0%, P < .001; pNN50 = adjusted R 2 : 99.5%, P < .001). The coefficient of determination of the regression equation for the frequency-domain variables was high without VLF (TP = adjusted R 2 : 75.0%, P < .001; LF = adjusted R 2 : 77.6%, P < .001; VLF = adjusted R 2 : 30.1%, P < .001; HF = adjusted R 2 : 71.3%, P < .001). Healthcare professionals, researchers, and the general public can quickly evaluate their psychological conditions using the HRV variables prediction equation.
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Taking the region around the Qinghai Lake as the study area and using the Landsat Thematic Mapper data and the measured grass yield data, the monadic linear regression models and the non-linear regression models were established, respectively, to express the relations between grassland biomass and the vegetation indices. There are two types of sampling site, i.e., the larger one is 30 m×30 m and the smaller one is 1 m×1 m. Each larger sampling site includes one smaller one which was randomly selected. The major conclusions from this study are: 1) the fitting accuracies of the non-linear regression models are much higher than those of the non-linear regression models, namely, the results obtained from the non-linear regression models are more accordant with the measured grassland biomass data in comparison with those from the monadic linear regression models; 2) the comparison of different forms of the non-linear regression analysis on the relations between the vegetation indices and the measured grassland biomass data indicates that the cubic equation is the best one in terms of the suitability of use in the study area; 3) the results from the non-linear regression analysis show that the order is RVI, NDVI, SAVI, MSAVI and DVI in terms of the fitting accuracy between these vegetation indices and grassland biomass data; and 4) the non-linear model Y = -18.626RVI3+220.317RVI2-648.271RVI+691.093 is the best model which can be used in monitoring grassland biomass based on the vegetation indices in the region around the Qinghai Lake.
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Baseline energy model is a model that relates the the energy consumption with its respective independent variables in a building. Prior to modelling, the selection of the independent variables was deemed important as it is the factor that governed the energy consumption. Without a proper analysis in selecting the independent variables, the development of the baseline energy model will suffer with impracticality and inaccuracy in prediction. Thus, this paper main objective is to analyze the independent variables that may affect the energy consumption in educational building before a baseline energy model will be developed. Single Linear Regression (SLR) model and Multiple Linear Regression (MLR) model will be used for the analysis purpose. Energy consumption data and independent variable data will be fed into the models. The coefficient of correlation (R) and coefficient of determination (R 2 ) value will be use to analyze the strength of the independent variables towards the energy consumption. Results show that the MLR model has a high value of R and R 2 0.91 and 0.84 respectively compared to SLR model that indicates more than one independent variables is affecting the energy consumption. Baseline energy models were developed from the SLR and MLR model where for the future work, energy consumption can be predicted using the baseline energy model.
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2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) (2021)
This paper analyzes the influencing factors of highway passenger and freight traffic, determines its influencing factors, and collects relevant statistical data from 117 different regions. Based on the principle of multiple linear regression method, first all variables are incorporated into the multiple regression equation for simulation. Second, integrate, demonstrate the applicability of the model, and then use the stepwise multiple regression method for model fitting. Based on this idea, the multiple linear regression model is constructed and forecasted for the highway passenger and freight volume. The results show that the stepwise multiple regression is effective. While the number of variables is greatly reduced and the calculation process is simplified, the model's fit is still good, and the problem of collinear between multiple variables is solved, and the regression coefficient of the variable is not consistent with the actual problem, and the result is predicted, It is also consistent with the actual situation and the applicability of the verification method, which can provide application references for road passenger and freight volume forecasting in other related areas.
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Experimental findings of the working capacity at a heart rate of 170 bts/min (W170) were compared to predicted values. Statistical tests were applied to examine the suitability and the error of prediction of three different regression models: a linear regression line, a polynomial regression model, and a "break point" regression model, which were compared to the time course of the heart rate during a linearly increasing work load from 0 to 100 W during 10 min. For this study the results of 28 children, 15 and 16 years old, and students of physical education were investigated. When a linear regression line was compared to these data, systematic deviations between measured data and the values estimated by this model were found. When the W170 was predicted using this model from the data collected during the first 10 min of an exercise procedure for the determination of the heart rate index, the physical working capacity was overestimated. The polynomial regression model and the "break point" regression model agreed with the time course of the heart rate without systematic error and allowed an unbiased prediction of the W170 from the first 10 min of the exercise test.
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Coke strength was predicted by selecting the indexes of blending coal quality.The indexes were used as independent variables for multiple linear regression analysis and got regression models.Then took regression coefficient to make t test,eliminated the no significant index and renewed to make the linear regression models.The research indicated that the model can be used to effectively predict coke strength and the relative error forecasting results was within 5%.
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Abstract This project examined the influence of the cadence, speed, heart rate and power towards the cycling performance by using Garmin Edge 1000. Any change in cadence will affect the speed, heart rate and power of the novice cyclist and the changes pattern will be observed through mobile devices installed with Garmin Connect application. Every results will be recorded for the next task which analysis the collected data by using machine learning algorithm which is Regression analysis. Regression analysis is a statistical method for modelling the connection between one or more independent variables and a dependent (target) variable. Regression analysis is required to answer these types of prediction problems in machine learning. Regression is a supervised learning technique that aids in the discovery of variable correlations and allows for the prediction of a continuous output variable based on one or more predictor variables. A total of forty days’ worth of events were captured in the dataset. Cadence act as dependent variable, (y) while speed, heart rate and power act as independent variable, (x) in prediction of the cycling performance. Simple linear regression is defined as linear regression with only one input variable (x). When there are several input variables, the linear regression is referred to as multiple linear regression. The research uses a linear regression technique to predict cycling performance based on cadence analysis. The linear regression algorithm reveals a linear relationship between a dependent (y) variable and one or more independent (y) variables, thus the name. Because linear regression reveals a linear relationship, it determines how the value of the dependent variable changes as the value of the independent variable changes. This analysis use the Mean Squared Error (MSE) expense function for Linear Regression, which is the average of squared errors between expected and real values. Value of R squared had been recorded in this project. A low R-squared value means that the independent variable is not describing any of the difference in the dependent variable-regardless of variable importance, this is letting know that the defined independent variable, although meaningful, is not responsible for much of the variance in the dependent variable’s mean. By using multiple regression, the value of R-squared in this project is acceptable because over than 0.7 and as known this project based on human behaviour and usually the R-squared value hardly to have more than 0.3 if involve human factor but in this project the R-squared is acceptable.
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The aim of the study is to achieve a statistical analysis of linear regression models of specific industrial processes data. The work strategy involves the regression analysis which is the most widely used statistical tools to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. This application focused on the fitting and checking of linear regression models, using small and large data sets, with pocket calculators or computers. The performance of regression analysis methods in practice depends on the form of the data generating process , and how it relates to the regression approach being used. It was used some statistical criteria as: Cochran criteria; Student criteria and Fischer criteria. After solving statistical analysis of the linear regression models, in the end there was obtained an applied statistical analysis of the linear regression model through the use of the classical method with a pocket computer. The same data were calculated with C++ software. By using this software we obtained more accurate results and the application time was reduced by several hours to 2-3 minutes.
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