BACKGROUND Major trauma is a leading cause of death worldwide. Evaluation of trauma care using Trauma Injury and Injury Severity Score (TRISS) method is focused in trauma outcome (deaths and survivors). For testing TRISS method TRISS misclassification rate is used. Calculating w-statistic, as a difference between observed and TRISS expected survivors, we compare our trauma care results with the...

For both clinical and research purposes, biopsies are used to classify liver damage known as fibrosis on an ordinal multi-state scale ranging from no damage to cirrhosis. Misclassification can arise from reading error (misreading of a specimen) or sampling error (the specimen does not accurately represent the liver). Studies of biopsy accuracy have not attempted to synthesize these two sources ...

To estimate the density or abundance of a cetacean species using acoustic detection data, it is necessary to correctly identify the species that are detected. Developing an automated species classifier with 100% correct classification rate for any species is likely to stay out of reach. It is therefore necessary to consider the effect of misidentified detections on the number of observed data a...

Exposure misclassification in case-control studies leads to bias in odds ratio estimates. There has been considerable interest recently to account for misclassification in estimation so as to adjust for bias as well as more accurately quantify uncertainty. These methods require users to elicit suitable values or prior distributions for the misclassification probabilities. In the event where exp...

aim : the aim of this study is to estimate gastric cancer (gc) mortality rate for iranian population, using bayesian approach in order to revise the existing classification which is thought to be a misclassification. background : gastric cancer (gc) is an important cause of mortality among many other types of cancer. data on cancer mortality can be used to guide policy makers in order to setup ...

Understanding the impact of vegetation mixture and misclassification on leaf area index (LAI) estimation is crucial for algorithm development and the application community. Using the MODIS standard land cover and LAI products, global LAI climatologies and statistics were obtained for both pure and mixed pixels to evaluate the effects of biome mixture on LAI estimation. Misclassification between...

In some real-world applications, it is time-consuming or expensive to collect much labeled data, while unlabeled data is easier to obtain. Many semi-supervised learning methods have been proposed to deal with this problem by utilizing the unlabeled data. On the other hand, on some datasets, misclassifying different classes causes different costs, which challenges the common assumption in classi...

Many previous studies on face recognition attempted to seek a precise classifier to achieve a low misclassification error, which is based on an assumption that all misclassification costs are the same. Inmany real-world scenarios, however, this assumption is not reasonable due to the imbalanced misclassification cost and insufficient high-quality facial image information. To address this issue,...

Genotypes of single nucleotide polymorphisms are subject to misclassification. If ignored, such misclassification can seriously distort the estimated genotype effects on the disease or outcome of interest. Validation data (gold standard or replicated surrogates) are required to assess the degree of misclassification and make adjustments. In practice, gold standard measurements may be unavailabl...

This paper illustrates how a misclassification cost matrix can be incorporated into an evolutionary classification system for bankruptcy prediction. Most classification systems for predicting bankruptcy have attempted to minimize misclassifications. The minimizing misclassification approach assumes that Type I and Type II error costs for misclassifications are equal. There is evidence that thes...