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What is Specificity

Chapter 2
Interventions for Improving Adaptive Behaviors in Children With Autism Spectrum Disorders
The proportion of negative diagnosis that are correctly identified.
Published in Chapter:
A Thorough Presentation of Autism Diagnostic Observation Schedule (ADOS-2)
Elpis Papaefstathiou (University of Macedonia, Greece)
Copyright: © 2022 |Pages: 18
DOI: 10.4018/978-1-7998-8217-6.ch002
Abstract
ADOS-2 is considered the gold standard observational instrument for use in the diagnosis and/or classification of autism and ASD. In this chapter, the process of assessment will be described, which involves direct observation and engagement of children and adults for whom an ASD is suspected. Specifically, an emphasis will be put on ADOS structure, namely the five different modules for the assessment. Then, the advantages of ADOS-2 will be elaborated as a diagnostic tool and a brief review of studies concerning its psychometric properties will be reported.
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Chapter 58
Characterization of Elevated Tumor Markers in Diagnosis of HCC Using Data Mining Methods
If the test diagnoses the patient without disease as not having disease, then it is referred to as specificity.
Published in Chapter: Characterization of Elevated Tumor Markers in Diagnosis of HCC Using Data Mining Methods; From: Encyclopedia of Information Science and Technology, Fifth Edition
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Chapter 189
ROC Analysis in Business Decision-makings
the probability that a diagnostic test can correctly specify a non-case.
Published in Chapter: ROC Analysis in Business Decision-makings; From: Encyclopedia of Business Analytics and Optimization
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Chapter 16
The Clinical Utility of Psychometric Tests: A Real-Data Approach From a Study Including Children With ADHD
The probability that the test result is negative for those participants with no clinical condition. A test that has high specificity will have few false positives results, but increases the false negative rate. Specificity is also known as True Negative Rate (TNR). Sensitivity and specificity are inversely related.
Published in Chapter: The Clinical Utility of Psychometric Tests: A Real-Data Approach From a Study Including Children With ADHD; From: Handbook of Research on Critical Issues in Special Education for School Rehabilitation Practices
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Chapter 40
The Globalization of Education and Control Techniques of the E-Learning Systems in Russian Smart Universities for Increase of Quality and Competitiveness
The structure and content of vocational education (all levels and at different levels)that is adequate to the actual needs of social production, expressed in professional standards. Acceptability and Realistic: for studying the conditions (in any place, at any time and through all my life) to obtain the Knowledge, Skills and Abilities to develop the necessary competences and qualifications of specialists, caused by the requirements of PS and specific for a certain period of time.
Published in Chapter: The Globalization of Education and Control Techniques of the E-Learning Systems in Russian Smart Universities for Increase of Quality and Competitiveness; From: Handbook of Research on Estimation and Control Techniques in E-Learning Systems
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Chapter 11
An AI Walk from Pharmacokinetics to Marketing
Success rate measure in a classification problem. If there are two classes (namely, positive and negative), specificity measures the ratio of negatives that are correctly classified by the model.
Published in Chapter: An AI Walk from Pharmacokinetics to Marketing; From: Encyclopedia of Artificial Intelligence
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Chapter 8
Risk of Business Bankruptcy: The Application of DEA Method – Case Study of Slovak Businesses
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Chapter 57
Using Receiver Operating Characteristic (ROC) Analysis to Evaluate Information-Based Decision-Making
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Chapter 20
Groupwise Non-Rigid Image Alignment Using Few Parameters: Registration of Facial and Medical Images
An error measure that identifies how the alignments help us represent the image set compactly.
Published in Chapter: Groupwise Non-Rigid Image Alignment Using Few Parameters: Registration of Facial and Medical Images; From: Handbook of Research on Human-Computer Interfaces and New Modes of Interactivity
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Chapter 132
Artificial Neural Networks in Medicine: Recent Advances
The percentage of patients who do not have a specific diagnosis or desired outcome that are correctly identified, which is the number of true negatives divided by the sum of the true negatives and false positives.
Published in Chapter: Artificial Neural Networks in Medicine: Recent Advances; From: Encyclopedia of Information Science and Technology, Fifth Edition
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Chapter 13
Text Separation From Document Images: A Deep Learning Approach
Specificity is a percentage of correctly classified normal samples, and it is given by the ratio of true negative to the addition of true negative and false positive samples.
Published in Chapter: Text Separation From Document Images: A Deep Learning Approach; From: Machine Learning and Deep Learning in Real-Time Applications
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Chapter 13
Algorithms for Detection and Classification of Abnormality in Mammograms: An Overview
Specificity is percentage of correctly classified normal samples and it is given by the ratio of true negative to the addition of true negative and false positive samples.
Published in Chapter: Algorithms for Detection and Classification of Abnormality in Mammograms: An Overview; From: Handbook of Research on Information Security in Biomedical Signal Processing
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Chapter 192
Using Receiver Operating Characteristic (ROC) Analysis to Evaluate Information -Based Decision-Making
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Chapter 128
Protecting Knowledge Assets
Organizational knowledge that loses some of its value when removed from its original application and firm.
Published in Chapter: Protecting Knowledge Assets; From: Encyclopedia of Knowledge Management, Second Edition
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Chapter 15
Best Practices in Dropout Prediction: Experience-Based Recommendations for Institutional Implementation
The performance metric for binary classifiers indicating the percentage of true negative cases correctly labelled as negative by the system.
Published in Chapter: Best Practices in Dropout Prediction: Experience-Based Recommendations for Institutional Implementation; From: Early Warning Systems and Targeted Interventions for Student Success in Online Courses
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Chapter 8
Machine Learning in Python: Diabetes Prediction Using Machine Learning
It is the ratio of true negatives to the sum of the true negatives and false positives.
Published in Chapter: Machine Learning in Python: Diabetes Prediction Using Machine Learning; From: Handbook of Research on Applications and Implementations of Machine Learning Techniques
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Chapter 21
Towards an Effective Imaging-Based Decision Support System for Skin Cancer
Measure of the ability of a clinician or decision support system to correctly identify those who do not have the disease.
Published in Chapter: Towards an Effective Imaging-Based Decision Support System for Skin Cancer; From: Handbook of Research on Applied Intelligence for Health and Clinical Informatics
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Chapter 5
Of Paradigms, Theories, and Models: A Conceptual Hierarchical Structure for Communication Science and Technoself
One end of a dimension defined by generality in evaluating paradigms, theories, and models (se Generality).
Published in Chapter: Of Paradigms, Theories, and Models: A Conceptual Hierarchical Structure for Communication Science and Technoself; From: Handbook of Research on Technoself: Identity in a Technological Society
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Chapter 1
Approaches of Early Detection of Autism Spectrum Disorders: A Brief Review
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Chapter 6
Surveillance Design After Initial Detection
True negative rate, the probability of not detecting a hazard when it is absent.
Published in Chapter: Surveillance Design After Initial Detection; From: Tactical Sciences for Biosecurity in Animal and Plant Systems
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Chapter 8
Systematic Literature Review: XAI and Clinical Decision Support
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Chapter 3
Brain Tumor Segmentation Using Deep Learning Technique: 2D U-Net Model Variant for Tumor Segmentation
It is the parameter used to assess a model's capability to predict the proportion of actual negative cases that were predicted by model as negative (or true negative).
Published in Chapter: Brain Tumor Segmentation Using Deep Learning Technique: 2D U-Net Model Variant for Tumor Segmentation; From: Recent Advancements in Smart Remote Patient Monitoring, Wearable Devices, and Diagnostics Systems
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