Data Mining Medical Information

2011 ◽  
pp. 92-107
Author(s):  
Thomas Chesney ◽  
Kay Penny ◽  
Peter Oakley ◽  
Simon Davies ◽  
David Chesney ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Medical Information ◽  
Predictive Accuracy ◽  
Outcome Analysis ◽  
Audit Data ◽  
Patient Will ◽  
Artificial Neural Network Ann ◽  
Dissemination Of Results

Trauma audit is intended to develop effective care for injured patients through process and outcome analysis, and dissemination of results. The system records injury details such as the patient’s sex and age, the mechanism of the injury, various measures of the severity of the injury, initial management and subsequent management interventions, and the outcome of the treatment including whether the patient lived or died. Ten years’ worth of trauma audit data from one hospital are modelled as an Artificial Neural Network (ANN) in order to compare the results with a more traditional logistic regression analysis. The output was set to be the probability that a patient will die. The ANN models and the logistic regression model achieve roughly the same predictive accuracy, although the ANNs are more difficult to interpret than the logistic regression model, and neither logistic regression nor the ANNs are particularly good at predicting death. For these reasons, ANNs are not seen as an appropriate tool to analyse trauma audit data. Results do suggest, however, the usefulness of using both traditional and non-traditional analysis techniques together and of including as many factors in the analysis as possible.

Data Mining Medical Information

2011 ◽  
pp. 2218-2231
Author(s):  
Thomas Chesney ◽  
Kay Penny ◽  
Peter Oakley ◽  
Simon Davies ◽  
David Chesney ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Medical Information ◽  
Predictive Accuracy ◽  
Outcome Analysis ◽  
Audit Data ◽  
Patient Will ◽  
Artificial Neural Network Ann ◽  
Dissemination Of Results

Trauma audit is intended to develop effective care for injured patients through process and outcome analysis, and dissemination of results. The system records injury details such as the patient’s sex and age, the mechanism of the injury, various measures of the severity of the injury, initial management and subsequent management interventions, and the outcome of the treatment including whether the patient lived or died. Ten years’ worth of trauma audit data from one hospital are modelled as an Artificial Neural Network (ANN) in order to compare the results with a more traditional logistic regression analysis. The output was set to be the probability that a patient will die. The ANN models and the logistic regression model achieve roughly the same predictive accuracy, although the ANNs are more difficult to interpret than the logistic regression model, and neither logistic regression nor the ANNs are particularly good at predicting death. For these reasons, ANNs are not seen as an appropriate tool to analyse trauma audit data. Results do suggest, however, the usefulness of using both traditional and non-traditional analysis techniques together and of including as many factors in the analysis as possible.

Data Mining Medical Information

2008 ◽  
pp. 2915-2927
Author(s):  
Thomas Chesney ◽  
Kay Penny ◽  
Peter Oakley ◽  
Simon Davies ◽  
David Chesney ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Medical Information ◽  
Predictive Accuracy ◽  
Outcome Analysis ◽  
Audit Data ◽  
Patient Will ◽  
Artificial Neural Network Ann ◽  
Dissemination Of Results

Trauma audit is intended to develop effective care for injured patients through process and outcome analysis, and dissemination of results. The system records injury details such as the patient’s sex and age, the mechanism of the injury, various measures of the severity of the injury, initial management and subsequent management interventions, and the outcome of the treatment including whether the patient lived or died. Ten years’ worth of trauma audit data from one hospital are modelled as an Artificial Neural Network (ANN) in order to compare the results with a more traditional logistic regression analysis. The output was set to be the probability that a patient will die. The ANN models and the logistic regression model achieve roughly the same predictive accuracy, although the ANNs are more difficult to interpret than the logistic regression model, and neither logistic regression nor the ANNs are particularly good at predicting death. For these reasons, ANNs are not seen as an appropriate tool to analyse trauma audit data. Results do suggest, however, the usefulness of using both traditional and non-traditional analysis techniques together and of including as many factors in the analysis as possible.

Relevance of Brain Regions’ Eloquence Assessment in Patients With a Large Ischemic Core Treated With Mechanical Thrombectomy

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000012863
Author(s):  
Basile Kerleroux ◽  
Joseph Benzakoun ◽  
Kévin Janot ◽  
Cyril Dargazanli ◽  
Dimitri Daly Eraya ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Mechanical Thrombectomy ◽  
Predictive Accuracy ◽  
Unmet Need ◽  
C Statistic ◽  
Ischemic Core ◽  
Functional Relevance

ObjectiveIndividualized patient selection for mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large ischemic core (LIC) at baseline is an unmet need.We tested the hypothesis, that assessing the functional relevance of both the infarcted and hypo-perfused brain tissue, would improve the selection framework of patients with LIC for MT.MethodsMulticenter, retrospective, study of adult with LIC (ischemic core volume > 70ml on MR-DWI), with MRI perfusion, treated with MT or best medical management (BMM).Primary outcome was 3-month modified-Rankin-Scale (mRS), favourable if 0-3. Global and regional-eloquence-based core-perfusion mismatch ratios were derived. The predictive accuracy for clinical outcome of eloquent regions involvement was compared in multivariable and bootstrap-random-forest models.ResultsA total of 138 patients with baseline LIC were included (MT n=96 or BMM n=42; mean age±SD, 72.4±14.4years; 34.1% females; mRS=0-3: 45.1%). Mean core and critically-hypo-perfused volume were 100.4ml±36.3ml and 157.6±56.2ml respectively and did not differ between groups. Models considering the functional relevance of the infarct location showed a better accuracy for the prediction of mRS=0-3 with a c-Statistic of 0.76 and 0.83 for logistic regression model and bootstrap-random-forest testing sets respectively. In these models, the interaction between treatment effect of MT and the mismatch was significant (p=0.04). In comparison in the logistic regression model disregarding functional eloquence the c-Statistic was 0.67 and the interaction between MT and the mismatch was insignificant.ConclusionConsidering functional eloquence of hypo-perfused tissue in patients with a large infarct core at baseline allows for a more precise estimation of treatment expected benefit.

Identification of Secondary Crash Risk Factors using Penalized Logistic Regression Model

2019 ◽  
Vol 2673 (11) ◽  
pp. 901-914 ◽  
Author(s):  
Angela E. Kitali ◽  
Priyanka Alluri ◽  
Thobias Sando ◽  
Wensong Wu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Real Time ◽  
Logistic Regression Model ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Mitigation Strategies ◽  
Study Results ◽  
Highly Correlated ◽  
Penalized Logistic Regression

Secondary crashes (SCs) have increasingly been recognized as a major problem leading to reduced capacity and additional traffic delays. However, the limited knowledge on the nature and characteristics of SCs has largely impeded their mitigation strategies. There are two main issues with analyzing SCs. First, relevant variables are unknown, but, at the same time, most of the variables considered in the models are highly correlated. Second, only a small proportion of incidents results in SCs, making it an imbalanced classification problem. This study developed a reliable SC risk prediction model using the Least Absolute Shrinkage and Selection Operator (LASSO) penalized logistic regression model with Synthetic Minority Oversampling TEchnique-Nominal Continuous (SMOTE-NC). The proposed model is considered to improve the predictive accuracy of the SC risk model because it accounts for the asymmetric nature of SCs, performs variable selection, and removes highly correlated variables. The study data were collected on a 35-mi I-95 section for 3 years in Jacksonville, Florida. SCs were identified based on real-time speed data. The results indicated that real-time traffic variables and primary incident characteristics significantly affect the likelihood of SCs. The most influential variables included mean of detector occupancy, coefficient of variation of equivalent hourly volume, mean of speed, primary incident type, percentage of lanes closed, incident occurrence time, shoulder blocked, number of responding agencies, incident impact duration, incident clearance duration, and roadway alignment. The study results can be used by agencies to develop SC mitigation strategies, and therefore improve the operational and safety performance of freeways.

scholarly journals Characterizing the Spatial Distribution of Eragrostis Curvula (Weeping Lovegrass) in New Jersey (United States of America) Using Logistic Regression

Environments ◽  
2019 ◽  
Vol 6 (12) ◽  
pp. 125
Author(s):  
Kikombo Ilunga Ngoy ◽  
Daniela Shebitz
Keyword(s):  
Invasive Species ◽  
Logistic Regression ◽  
New Jersey ◽  
Regression Model ◽  
Goodness Of Fit ◽  
Logistic Regression Model ◽  
Invasive Plant ◽  
Predictive Accuracy ◽  
Global Environmental Change ◽  
Eragrostis Curvula

The increasing spread of invasive plants has become a critical driver of global environmental change. Once established, invasive species are often impossible to eradicate. Therefore, predicting the spread has become a key element in fighting invasive species. In this study, we examined the efficiency of a logistic regression model as a tool to identify the spatial occurrence of an invasive plant species. We used Eragrostis curvula (Weeping Lovegrass) as the dependent variable. The independent variables included temperature, precipitation, soil types, and the road network. We randomly selected 68 georeferenced points to test the goodness of fit of the logistic regression model to predict the presence of E. curvula. We validated the model by selecting an additional 68 random points. Results showed that the probability to successfully predict the presence of E. Curvula was 82.35%. The overall predictive accuracy of the model for the presence or absence of E. Curvula was 80.88%. Additional tests including the Chi-square test, the Hosmer–Lemeshow (HL) test, and the area under the curve (AUC) values, all indicated that the model was the best fit. Our results showed that E. curvula was associated with the identified variables. This study suggests that the logistic regression model can be a useful tool in the identification of invasive species in New Jersey.

scholarly journals Predicting Egyptian Banks Distress

2018 ◽  
Vol 8 (3) ◽  
pp. 39 ◽  
Author(s):  
Osama EL-Ansary ◽  
Mohamed Saleh
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Regression Model ◽  
Data Base ◽  
Financial Distress ◽  
Prediction Accuracy ◽  
Logistic Regression Model ◽  
Predictive Accuracy ◽  
Prediction Method ◽  
Accurate Prediction

Purpose – the main purpose of the study is to investigate an accurate prediction method for banking distress applied on a set of Egyptian banks.Methodology - the researchers have compared the prediction accuracy of the discriminant analysis and logistic regression model, to choose the most appropriate one. The data has been collected from the “Bank scope” data base and for the period of 2002–2016.Findings – the results of the study revealed that the predictive accuracy of discriminant analysis outperformed that of the logistic regression model.Originality - The study adds value to the literature as it is one of the few studies that is concerned with predicating the banking financial distress especially in Egypt.

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