|
Positive Predictive Value
The positive and negative predictive values (PPV and NPV respectively) are the proportions of positive and negative results in statistics and diagnostic tests that are true positive and true negative results, respectively. The PPV and NPV describe the performance of a diagnostic test or other statistical measure. A high result can be interpreted as indicating the accuracy of such a statistic. The PPV and NPV are not intrinsic to the test (as true positive rate and true negative rate are); they depend also on the prevalence. Both PPV and NPV can be derived using Bayes' theorem. Although sometimes used synonymously, a ''positive predictive value'' generally refers to what is established by control groups, while a post-test probability refers to a probability for an individual. Still, if the individual's pre-test probability of the target condition is the same as the prevalence in the control group used to establish the positive predictive value, the two are numerically equal. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Positive And Negative Predictive Values
The positive and negative predictive values (PPV and NPV respectively) are the proportions of positive and negative results in statistics and diagnostic tests that are true positive and true negative results, respectively. The PPV and NPV describe the performance of a diagnostic test or other statistical measure. A high result can be interpreted as indicating the accuracy of such a statistic. The PPV and NPV are not intrinsic to the test (as true positive rate and true negative rate are); they depend also on the prevalence. Both PPV and NPV can be derived using Bayes' theorem. Although sometimes used synonymously, a ''positive predictive value'' generally refers to what is established by control groups, while a post-test probability refers to a probability for an individual. Still, if the individual's pre-test probability of the target condition is the same as the prevalence in the control group used to establish the positive predictive value, the two are numerically equal ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
False Discovery Rate
In statistics, the false discovery rate (FDR) is a method of conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons. FDR-controlling procedures are designed to control the FDR, which is the expected proportion of "discoveries" (rejected null hypotheses) that are false (incorrect rejections of the null). Equivalently, the FDR is the expected ratio of the number of false positive classifications (false discoveries) to the total number of positive classifications (rejections of the null). The total number of rejections of the null include both the number of false positives (FP) and true positives (TP). Simply put, FDR = FP / (FP + TP). FDR-controlling procedures provide less stringent control of Type I errors compared to family-wise error rate (FWER) controlling procedures (such as the Bonferroni correction), which control the probability of ''at least one'' Type I error. Thus, FDR-controlling procedures have greater power, at t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Receiver-operator Characteristic
A receiver operating characteristic curve, or ROC curve, is a graphical plot that illustrates the performance of a binary classifier model (can be used for multi class classification as well) at varying threshold values. ROC analysis is commonly applied in the assessment of diagnostic test performance in clinical epidemiology. The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting. The ROC can also be thought of as a plot of the statistical power as a function of the Type I Error of the decision rule (when the performance is calculated from just a sample of the population, it can be thought of as estimators of these quantities). The ROC curve is thus the sensitivity as a function of false positive rate. Given that the probability distributions for both true positive and false positive are known, the ROC curve is obtained as the cumulative distribution function (CDF, area under the probability distribution fr ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Relevance (information Retrieval)
In information science and information retrieval, relevance denotes how well a retrieved document or set of documents meets the information need of the user. Relevance may include concerns such as timeliness, authority or novelty of the result. History The concern with the problem of finding relevant information dates back at least to the first publication of scientific journals in the 17th century. The formal study of relevance began in the 20th century with the study of what would later be called bibliometrics. In the 1930s and 1940s, S. C. Bradford used the term "relevant" to characterize articles relevant to a subject (cf., Bradford's law). In the 1950s, the first information retrieval systems emerged, and researchers noted the retrieval of irrelevant articles as a significant concern. In 1958, B. C. Vickery made the concept of relevance explicit in an address at the International Conference on Scientific Information. Since 1958, information scientists have explored and ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
False Discovery Rate
In statistics, the false discovery rate (FDR) is a method of conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons. FDR-controlling procedures are designed to control the FDR, which is the expected proportion of "discoveries" (rejected null hypotheses) that are false (incorrect rejections of the null). Equivalently, the FDR is the expected ratio of the number of false positive classifications (false discoveries) to the total number of positive classifications (rejections of the null). The total number of rejections of the null include both the number of false positives (FP) and true positives (TP). Simply put, FDR = FP / (FP + TP). FDR-controlling procedures provide less stringent control of Type I errors compared to family-wise error rate (FWER) controlling procedures (such as the Bonferroni correction), which control the probability of ''at least one'' Type I error. Thus, FDR-controlling procedures have greater power, at t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Binary Classification
Binary classification is the task of classifying the elements of a set into one of two groups (each called ''class''). Typical binary classification problems include: * Medical testing to determine if a patient has a certain disease or not; * Quality control in industry, deciding whether a specification has been met; * In information retrieval, deciding whether a page should be in the result set of a search or not * In administration, deciding whether someone should be issued with a driving licence or not * In cognition, deciding whether an object is food or not food. When measuring the accuracy of a binary classifier, the simplest way is to count the errors. But in the real world often one of the two classes is more important, so that the number of both of the different types of errors is of interest. For example, in medical testing, detecting a disease when it is not present (a '' false positive'') is considered differently from not detecting a disease when it is present (a '' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sore Throat
Sore throat, also known as throat pain, is pain or irritation of the throat. The majority of sore throats are caused by a virus, for which antibiotics are not helpful. For sore throat caused by bacteria (GAS), treatment with antibiotics may help the person get better faster, reduce the risk that the bacterial infection spreads, prevent retropharyngeal abscesses and quinsy, and reduce the risk of other complications such as rheumatic fever and rheumatic heart disease. In most developed countries, post-streptococcal diseases have become far less common. For this reason, awareness and public health initiatives to promote minimizing the use of antibiotics for viral infections have become the focus. Approximately 35% of childhood sore throats and 5–25% of cases in adults are caused by a bacterial infection from group A streptococcus. Sore throats that are "non-group A streptococcus" are assumed to be caused by a viral infection. Sore throat is a common reason for people to vi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Likelihood Ratios In Diagnostic Testing
In evidence-based medicine, likelihood ratios are used for assessing the value of performing a diagnostic test. They combine sensitivity and specificity into a single metric that indicates how much a test result shifts the probability that a condition (such as a disease) is present. The first description of the use of likelihood ratios for decision rules was made at a symposium on information theory in 1954. In medicine, likelihood ratios were introduced between 1975 and 1980.. There is a multiclass version of these likelihood ratios. Calculation Two versions of the likelihood ratio exist, one for positive and one for negative test results. Respectively, they are known as the (LR+, likelihood ratio positive, likelihood ratio for positive results) and (LR–, likelihood ratio negative, likelihood ratio for negative results). The positive likelihood ratio is calculated as : \text+ = \frac which is equivalent to : \text+ = \frac or "the probability of a person who has t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Prevalence Threshold
In medicine and statistics, sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a medical condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true positives and specificity is a measure of how well a test can identify true negatives: * Sensitivity (true positive rate) is the probability of a positive test result, conditioned on the individual truly being positive. * Specificity (true negative rate) is the probability of a negative test result, conditioned on the individual truly being negative. If the true status of the condition cannot be known, sensitivity and specificity can be defined relative to a " gold standard test" which is assumed correct. For all testing, both diagnoses and screening, there is usually a trade-off between sensitivity and specificity, such that higher sensiti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Post-test Probabilities
Pre-test probability and post-test probability (alternatively spelled pretest and posttest probability) are the probabilities of the presence of a condition (such as a disease) before and after a diagnostic test, respectively. ''Post-test probability'', in turn, can be ''positive'' or ''negative'', depending on whether the test falls out as a positive test or a negative test, respectively. In some cases, it is used for the probability of developing the condition of interest in the future. Test, in this sense, can refer to any medical test (but usually in the sense of diagnostic tests), and in a broad sense also including questions and even assumptions (such as assuming that the target individual is a female or male). The ability to make a difference between pre- and post-test probabilities of various conditions is a major factor in the indication of medical tests. Pre-test probability The pre-test probability of an individual can be chosen as one of the following: *The prevalenc ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Likelihood Ratios In Diagnostic Testing
In evidence-based medicine, likelihood ratios are used for assessing the value of performing a diagnostic test. They combine sensitivity and specificity into a single metric that indicates how much a test result shifts the probability that a condition (such as a disease) is present. The first description of the use of likelihood ratios for decision rules was made at a symposium on information theory in 1954. In medicine, likelihood ratios were introduced between 1975 and 1980.. There is a multiclass version of these likelihood ratios. Calculation Two versions of the likelihood ratio exist, one for positive and one for negative test results. Respectively, they are known as the (LR+, likelihood ratio positive, likelihood ratio for positive results) and (LR–, likelihood ratio negative, likelihood ratio for negative results). The positive likelihood ratio is calculated as : \text+ = \frac which is equivalent to : \text+ = \frac or "the probability of a person who has t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fecal Occult Blood
Fecal occult blood (FOB) refers to blood in the feces that is not visibly apparent (unlike other types of blood in stool such as melena or hematochezia). A fecal occult blood test (FOBT) checks for hidden (occult) blood in the stool (feces). The American College of Gastroenterology has recommended the abandoning of gFOBT testing as a colorectal cancer screening tool, in favor of the fecal immunochemical test (FIT). The newer and recommended tests look for globin, DNA, or other blood factors including transferrin, while conventional stool guaiac tests look for heme. Medical uses Fecal occult blood testing (FOBT), as its name implies, aims to detect subtle blood loss in the gastrointestinal tract, anywhere from the mouth to the colon. Positive tests ("positive stool") may result from either upper gastrointestinal bleeding or lower gastrointestinal bleeding and warrant further investigation for peptic ulcers or a malignancy (such as colorectal cancer or gastric cancer). T ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
