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People Versus Computers in Medicine
Published in Marilyn Sue Bogner, Human Error in Medicine, 2018
Thomas B. Sheridan, James M. Thompson
Neural network systems have been proposed and tested in a variety of clinical situations (Baxt, 1991; Miller, Blott, & Hames, 1992; Weinstein et. al., 1993). They are becoming increasingly accurate, and thus have become successful in providing a valuable second opinion in clinical situations. In a blinded, prospective study, Baxt (1991) compared the accuracy of the physicians and an artificial neural network in the diagnosis of acute myocardial infarction (heart attack) in 331 patients presenting to an emergency room. The physicians had a diagnostic sensitivity of 77.7% and a diagnostic specificity of 84.7%. The neural network had a sensitivity of 97.2% and a specificity of 96.2%. Sensitivity is the probability of a positive test result for a patient who has the diagnosis under consideration (also known as true-positive). Specificity is the probability of a negative test result in a patient who does not have the diagnosis under consideration (also known as true-negative).
Adaptive Pillar K Means Algorithm To Detect Colon Cancer From Biopsy Samples
Published in T. Kishore Kumar, Ravi Kumar Jatoth, V. V. Mani, Electronics and Communications Engineering, 2019
B. Saroja, A. Selwin Mich Priyadharson
Specificity is a measure of the ability of a technique to correctly identify negative samples. It can be calculated using the following equation: Specificity=TNTN+FP
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Published in Sandeep Nema, John D. Ludwig, Parenteral Medications, 2019
Justine Young, Brandon Zurawlow
Specificity is generally defined as the method’s ability to identify the analyte in the presence of other components. Through the lens of CCI testing, consider that the analyte is always leakage. Thus, specificity becomes the ability of a method to identify leakage in the presence of confounding variables.
Cricket fast bowling detection in a training setting using an inertial measurement unit and machine learning
Published in Journal of Sports Sciences, 2019
Joseph W. McGrath, Jonathon Neville, Tom Stewart, John Cronin
The performance of each model was evaluated by computing the sensitivity, specificity, accuracy, and F1 score of the predictions made on the test set. Sensitivity refers to the proportion of positive cases that are correctly identified (e.g., proportion of bowling events identified as bowling), while specificity refers to the proportion of negative cases correctly identified (i.e., proportion of non-bowling events identified as such). The accuracy is the proportion of correct predictions. The F1 score (Forman & Scholz, 2010) was computed as a supplementary measure because, unlike accuracy, it is not influenced by class distribution (our dataset contained more bowls than throws). To examine the contribution of each feature to model performance, the “varImp” function in the “caret” package was applied to each model. This function computes a ROC curve for each predictor, and uses the area under the ROC curve as an indicator of feature importance. Models were compared using the accuracy metric, and were deemed significantly different if the 95% confidence intervals did not overlap. The training times of each model were compared using an ANOVA and TukeyHSD post hoc test where applicable.
IoT-based patient stretcher movement simulation in smart hospital using type-2 fuzzy sets systems
Published in Production Planning & Control, 2023
C. B. Sivaparthipan, M. Anand, Nidhi Agarwal, Mallika Dhingra, Laxmi Raja, Akila Victor, S. A. Amala Nirmal Doss
In Figure 12, sensitivity is the quality of being tender, easily irritated, or sympathetic. Specificity refers to a test’s accuracy in identifying those who do not have a condition or characteristic. The comparison result is that the proposed patient stretcher movement simulation system is approximately 49.6% efficient.
Fractional gravitational search-radial basis neural network for bone marrow white blood cell classification
Published in The Imaging Science Journal, 2018
Namdev Devidas Pergad, Satish T. Hamde
Specificity: Specificity is defined as the ratio of correctly classified negative to the sum of the correctly classified positive and negative. It is referred to as the false-positive rate. And it is given by