scholarly journals ROC Estimation from Clustered Data with an Application to Liver Cancer Data

2016 ◽  
Vol 15s4 ◽  
pp. CIN.S40299
Author(s):  
Joungyoun Kim ◽  
Sung-Cheol Yun ◽  
Johan Lim ◽  
Moo-Song Lee ◽  
Won Son ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Diagnostic Methods ◽  
Time Data ◽  
Cancer Data ◽  
Practical Usefulness ◽  
Proposed Model ◽  
Magnetic Resonance Imaging Mri ◽  
Survival Time Data ◽  
Test Outcomes ◽  
Ordinal Test

In this article, we propose a regression model to compare the performances of different diagnostic methods having clustered ordinal test outcomes. The proposed model treats ordinal test outcomes (an ordinal categorical variable) as grouped-survival time data and uses random effects to explain correlation among outcomes from the same cluster. To compare different diagnostic methods, we introduce a set of covariates indicating diagnostic methods and compare their coefficients. We find that the proposed model defines a Lehmann family and can also introduce a location-scale family of a receiver operating characteristic (ROC) curve. The proposed model can easily be estimated using standard statistical software such as SAS and SPSS. We illustrate its practical usefulness by applying it to testing different magnetic resonance imaging (MRI) methods to detect abnormal lesions in a liver.

scholarly journals A Dynamic Mobility Traffic Model Based on Two Modes of Transport in Smart Cities

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 253-270
Author(s):  
Mohammed Bin Hariz ◽  
Dhaou Said ◽  
Hussein T. Mouftah
Keyword(s):  
Data Exchange ◽  
Smart Cities ◽  
Time Data ◽  
School Bus ◽  
The Public ◽  
Ride Sharing ◽  
Dynamic Mobility ◽  
Proposed Model ◽  
Loading System ◽  
Suitable Combination

This paper focuses on transportation models in smart cities. We propose a new dynamic mobility traffic (DMT) scheme which combines public buses and car ride-sharing. The main objective is to improve transportation by maximizing the riders’ satisfaction based on real-time data exchange between the regional manager, the public buses, the car ride-sharing and the riders. OpenStreetMap and OMNET++ were used to implement a realistic scenario for the proposed model in a city like Ottawa. The DMT scheme was compared to a multi-loading system used for a school bus. Simulations showed that rider satisfaction was enhanced when a suitable combination of transportation modes was used. Additionally, compared to the other scheme, this DMT scheme can reduce the stress level of car ride-sharing and public buses during the day to the minimal level.

Development and validation of a prognostic model for survival time data: application to prognosis of HIV positive patients treated with antiretroviral therapy

2004 ◽  
Vol 23 (15) ◽  
pp. 2375-2398 ◽  
Author(s):  
Margaret May ◽  
Patrick Royston ◽  
Matthias Egger ◽  
Amy C. Justice ◽  
Jonathan AC Sterne ◽  
...  
Keyword(s):  
Antiretroviral Therapy ◽  
Survival Time ◽  
Prognostic Model ◽  
Hiv Positive ◽  
Time Data ◽  
Data Application ◽  
Development And Validation ◽  
Survival Time Data

Interactions of increased pO2 and pCO2 effects in producing convulsions and death in mice

1961 ◽  
Vol 16 (1) ◽  
pp. 1-7 ◽  
Author(s):  
John R. Marshall ◽  
Christian J. Lambertsen
Keyword(s):  
Carbon Dioxide ◽  
High Pressure ◽  
Blood Flow ◽  
Latent Period ◽  
Survival Time ◽  
Brain Blood Flow ◽  
Time Data ◽  
Cortical Depression ◽  
Survival Time Data ◽  
Very High

In 379 mice subjected to from 1 to 11 atm. of pO2 and 0 to 304 mm Hg of pCO2 for 90 minutes, oxygen was convulsigenic at pressures greater than 3 atm. and lethal at greater than 4 atm. Carbon dioxide in 1 atm. of O2 was not convulsigenic but was lethal at very high tensions. In the presence of O2 at high pressure (OHP) small elevations of CO2 tension shortened the preconvulsive latent period, whereas CO2 tensions greater than 120 mm Hg inhibited convulsions. Survival time in OHP was shortened by the addition of CO2. An interaction between OHP and CO2 effects is suggested by both the preconvulsive latent period and survival time data. The effects of CO2 on OHP and electroshock convulsions are compared and possible reasons for differences are discussed in light of the previously demonstrated general cortical depression and inhibition of convulsions by CO2. The potentiation of OHP convulsions by low CO2 tensions is probably due to effects on brain blood flow. Although death can occur without convulsions there is a tendency for animals susceptible to convulsions to be also susceptible to the lethal properties of OHP with CO2. Submitted on July 28, 1960

Correlated gamma frailty models for bivariate survival time data

2018 ◽  
Vol 28 (10-11) ◽  
pp. 3437-3450
Author(s):  
Adelino Martins ◽  
Marc Aerts ◽  
Niel Hens ◽  
Andreas Wienke ◽  
Steven Abrams
Keyword(s):  
Survival Time ◽  
Frailty Models ◽  
Current Status Data ◽  
Current Status ◽  
Mortality Data ◽  
Time Data ◽  
Additive Decomposition ◽  
Bivariate Survival ◽  
Gamma Frailty ◽  
Survival Time Data

Frailty models have been developed to quantify both heterogeneity as well as association in multivariate time-to-event data. In recent years, numerous shared and correlated frailty models have been proposed in the survival literature allowing for different association structures and frailty distributions. A bivariate correlated gamma frailty model with an additive decomposition of the frailty variables into a sum of independent gamma components was introduced before. Although this model has a very convenient closed-form representation for the bivariate survival function, the correlation among event- or subject-specific frailties is bounded above which becomes a severe limitation when the values of the two frailty variances differ substantially. In this article, we review existing correlated gamma frailty models and propose novel ones based on bivariate gamma frailty distributions. Such models are found to be useful for the analysis of bivariate survival time data regardless of the censoring type involved. The frailty methodology was applied to right-censored and left-truncated Danish twins mortality data and serological survey current status data on varicella zoster virus and parvovirus B19 infections in Belgium. From our analyses, it has been shown that fitting more flexible correlated gamma frailty models in terms of the imposed association and correlation structure outperforms existing frailty models including the one with an additive decomposition.

Agreement between magnetic resonance imaging, myelography, and surgery for detecting recurrent, thoracolumbar intervertebral disc extrusion in dogs

2013 ◽  
Vol 26 (01) ◽  
pp. 12-18 ◽  
Author(s):  
B. A. Brisson ◽  
S. G. Nykamp ◽  
D. Reynolds
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Intervertebral Disc ◽  
Interobserver Agreement ◽  
Surgical Decompression ◽  
Diagnostic Methods ◽  
Resonance Imaging ◽  
Disc Disease ◽  
Recurrent Lesion ◽  
Magnetic Resonance Imaging Mri

Summary Objectives: Although magnetic resonance imaging (MRI) is reported to be superior to myelography to determine the location and site of first time disc herniation, comparison of these diagnostic methods in cases of recurrent intervertebral disc disease (IVD) herniation after a first surgery has not been evaluated. The objective was to compare the diagnostic accuracy of MRI and myelography in a series of dogs undergoing repeat surgical decompression for recurrent IVD extrusion when compared to the gold standard of surgery. Methods: Ten dogs with recurrent IVD herniation underwent MRI and myelography followed by surgical decompression. Three observers reviewed the images to determine the site and side of the first surgery and the recurrent lesion. Agreement was determined by calculating a kappa (κ) score. Results: Substantial interobserver agreement was noted for recurrent lesion site using MRI and myelography (κ = 0.77 vs. 0.73) and when comparing MRI and myelography to the reported surgical site (κ = 0.73 vs. 0.67). Interobserver agreement was greater with MRI for circumferential location compared to myelography (κ = 0.76 vs. 0.43), similar to what was found when comparing to surgical side (κ = 0.82 vs. 0.49). The previous surgical site in this study had no effect on ability to identify the new lesion. Clinical significance: Despite the limitations of MRI, there was greater agreement between observers using MRI for both the recurrent and first lesion.

scholarly journals Segmentation and Classification of Breast Cancer Tumour

2018 ◽  
Vol 2 (1) ◽  
pp. 14-18
Author(s):  
Gokalp Cinarer ◽  
Bulent Gursel Emiroglu ◽  
Ahmet Hasim Yurttakal
Keyword(s):  
Breast Cancer ◽  
Texture Features ◽  
Computer Aided Diagnosis ◽  
Wavelet Approximation ◽  
Proposed Model ◽  
Svm Algorithm ◽  
Computer Aided ◽  
Magnetic Resonance Imaging Mri ◽  
Aided Diagnosis

Breast cancer is cancer that forms in the cells of the breasts. Breast cancer is the most common cancer diagnosed in women in the world. Breast cancer can occur in both men and women, but it's far more common in women. Early detection of breast cancer tumours is crucial in the treatment. In this study, we presented a computer aided diagnosis expectation maximization segmentation and co-occurrence texture features from wavelet approximation tumour image of each slice and evaluated the performance of SVM Algorithm. We tested the model on 50 patients, among them, 25 are benign and 25 malign. The 80% of the images are allocated for training and 20% of images reserved for testing. The proposed model classified 2 patients correctly with success rate of 80% in case of 5 Fold Cross-Validation  Keywords: Breast Cancer, Computer-Aided Diagnosis (CAD), Magnetic Resonance Imaging (MRI);

scholarly journals PCA based Regression Decision Tree Classification for Somatic Mutations.

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1095-1102
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
Somatic Mutations ◽  
Feature Selection Technique ◽  
Data Set ◽  
Cancer Data ◽  
Classification Feature ◽  
Decision Tree Classification ◽  
Proposed Model ◽  
Feature Selection Techniques

The analization of cancer data and normal data for the predication of somatic mu-tation occurrences in the data set plays an important role and several challenges persist in detectingsomatic mutations which leads to complexity of handling large volumes of data in classifi-cation with good accuracy. In many situations the dataset may consist of redundant and less significant features and there is a need to remove insignificant features in order to improve the performance of classification. Feature selection techniques are useful for dimensionality reduction purpose. PCA is one type of feature selection technique to identify significant attributes and is adopted in this paper. A novel technique, PCA based regression decision tree is proposed for classification of somatic mutations data in this paper.The performance analysis of this clas-sification process for the detection of somatic mutation is compared with existing algorithms and satisfactory results are obtained with the proposed model.

scholarly journals Classification & Feature extraction of Brain tumor from MRI Images using Modified ANN Approach

2021 ◽  
Vol 9 (2) ◽  
pp. 10-15
Author(s):  
Harendra Singh ◽  
Roop Singh Solanki
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Classification Feature ◽  
Proposed Model ◽  
Magnetic Resonance Imaging Mri ◽  
Artificial Neural Network Ann ◽  
High Degree

In this research paper, a new modified approach is proposed for brain tumor classification as well as feature extraction from Magnetic Resonance Imaging (MRI) after pre-processing of the images. The discrete wavelet transformation (DWT) technique is used for feature extraction from MRI images and Artificial Neural Network (ANN) is used for the classification of the type of tumor according to extracted features. Mean, Standard deviation, Variance, Entropy, Skewness, Homogeneity, Contrast, Correlation are the main features used to classify the type of tumor. The proposed model can give a better result in comparison with other available techniques in less computational time as well as a high degree of accuracy. The training and testing accuracies of the proposed model are 100% and 98.20% with a 98.70 % degree of precision respectively.

Serum diiodotyrosine—a biomarker to differentiate destructive thyroiditis from Graves’ disease

2021 ◽  
Author(s):  
Naoya Fujita ◽  
Yosuke Ono ◽  
Azusa Sano ◽  
Motohiro Kimata ◽  
Seigo Oyama ◽  
...  
Keyword(s):  
Roc Curve ◽  
Operating Characteristic ◽  
Cross Sectional Study ◽  
Curve Analysis ◽  
Diagnostic Methods ◽  
Graves Disease ◽  
Roc Curve Analysis ◽  
Cross Sectional ◽  
Chromatography Tandem Mass Spectrometry ◽  
Liquid Chromatography Tandem Mass

Objective: Conventional diagnostic methods are limited in their ability to differentiate destructive thyroiditis from Graves’ disease. We hypothesised that serum diiodotyrosine (DIT) and monoiodotyrosine (MIT) levels could be biomarkers for differentiating destructive thyroiditis from Graves’ disease. Design: Patients with destructive thyroiditis (n = 13) and Graves’ disease (n = 22) were enrolled in this cross-sectional study. Methods: We assayed the serum DIT and MIT levels using liquid chromatography-tandem mass spectrometry. A receiver operating characteristic (ROC) curve analysis was used to determine the sensitivity and specificity of the serum DIT and MIT levels as biomarkers for differentiating destructive thyroiditis from Graves’ disease. Results: The serum DIT and MIT levels were significantly higher in patients with destructive thyroiditis than in those with Graves’ disease. The ROC curve analysis showed that the serum DIT levels (≥ 359.9 pg/mL) differentiated destructive thyroiditis from Graves’ disease, significantly, with 100.0% sensitivity and 95.5% specificity (P < .001). The diagnostic accuracy of the serum MIT levels (≥119.4 pg/mL) was not as high as that of the serum DIT levels (sensitivity, 84.6%; specificity, 77.3%; P = .001). Conclusions: The serum DIT levels may serve as a novel diagnostic biomarker for differentiating destructive thyroiditis from Graves’ disease.

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