Utilizing Deep Learning and Oversampling Methods to Identify Children’s Emotional and Behavioral Risk

2020 ◽  
pp. 073428292095172
Author(s):  
Jiandong Wang ◽  
Jin Liu ◽  
Christine DiStefano ◽  
Gaofeng Pan ◽  
Ruiqin Gao ◽  
...  
Keyword(s):  
School Environment ◽  
Rating Scale ◽  
Teacher Rating ◽  
True Positive Rate ◽  
Assessment System ◽  
Behavioral Disorder ◽  
Behavioral Risk ◽  
True Negative ◽  
Positive Rate ◽  
Novel Method

Deep neural network (DNN) has been widely used in various artificial intelligence applications and is, unsurprisingly, penetrating the field of school psychology. In the school environment, universal screening is used by teachers to identify children’s emotional and behavioral risk (EBR) within a screener. EBR can be used to predict possible emotional and behavioral disorders, which impact children’s educational and social outcomes. Using the BASC-2 Behavioral and Emotional Screening System Teacher Rating Scale (BASC-2 BESS TRS; Reynolds & Kamphaus (2004). Behavior Assessment System for Children (2nd ed.). Circle Pines, MN: American Guidance Service) norm data, we classified children’s EBR status from normal to at-risk using DNN. Data oversampling was used to overcome the imbalanced sample feature (i.e., few cases with emotional and behavioral disorder). Traditional machine learning methods, such as Naïve Bayes and logistic regression, were included for comparison. The results indicated that the DNN with oversampling achieved the highest performance levels with accuracy (ACC) of .957, precision (PPV) of .545, true positive rate (TPR or sensitivity) of 1.000, and true negative rate (TNR or specificity) of .942 compared with the other methods. This novel method is helpful to provide accurate screening results for early identification of children’s EBR. The current study provides a useful guide for researchers to apply the DNN and oversampling to classification in assessment-related research.

Behavior Assessment System for Children Teacher Rating Scale–Child—Short Form

2007 ◽  
Author(s):  
Randy W. Kamphaus ◽  
Jennifer S. Thorpe ◽  
Anne Pierce Winsor ◽  
Anna P. Kroncke ◽  
Erin T. Dowdy ◽  
...  
Keyword(s):  
Rating Scale ◽  
Short Form ◽  
Teacher Rating ◽  
Assessment System ◽  
Behavior Assessment ◽  
Teacher Rating Scale

Performance of Fineneedle Aspiration Cytology of the Breast. Clinical Experience in Ravenna (Italy)

1993 ◽  
Vol 79 (6) ◽  
pp. 413-417
Author(s):  
Lauro Bucchi ◽  
Patrizia Schincaglia ◽  
Giangiuseppe Melandri ◽  
Nori Morini ◽  
Carlo Naldoni ◽  
...  
Keyword(s):  
Predictive Value ◽  
Breast Cancer Case ◽  
True Positive Rate ◽  
Cancer Case ◽  
Breast Lesions ◽  
Additional Contribution ◽  
True Positive ◽  
Aspiration Cytology ◽  
True Negative ◽  
Positive Rate

Aims and background Fineneedle aspiration cytology (FNAC) is a routine test in the evaluation of breast lesions. We assessed the diagnostic accuracy of mammography (MG), physical examination (PE), ultrasonography (US) and FNAC in 1064 histologically confirmed breast lesions (638 malignant, 426 benign) observed consecutively at the Cancer Prevention Center of Ravenna (Italy). Methods The performance of each test and the additional contribution of FNAC were determined. Results FNAC was done in 69.6 % of cancers and 39.7 % of benign lesions (P = 0.00000), the frequency of aspiration being significantly associated with severity at MG, PE, and US. For FNAC, the true positive rate was 95.1 % and the true negative rate 67.4 %. Only one breast cancer case was detected by FNAC alone (additional true positive rate 0.2 %). The positive predictive value of FNAC in the absence of other abnormalities was 5 %. The negative predictive value of a benign report at MG, PE, US and FNAC was 100 %. Conclusions All breast lesions should be evaluated by all available techniques, especially FNAC, and open biopsy should be avoided for those reported as benign at all tests.

scholarly journals The Validity of the Tool “statcheck” in Discovering Statistical Reporting Inconsistencies

2017 ◽  
Author(s):  
Michele B. Nuijten ◽  
Marcel A. L. M. van Assen ◽  
Chris Hubertus Joseph Hartgerink ◽  
Sacha Epskamp ◽  
Jelte M. Wicherts
Keyword(s):  
Multiple Testing ◽  
Degrees Of Freedom ◽  
R Package ◽  
True Positive Rate ◽  
P Value ◽  
True Positive ◽  
Test Statistic ◽  
True Negative ◽  
True Negative Rate ◽  
Positive Rate

The R package “statcheck” (Epskamp & Nuijten, 2016) is a tool to extract statistical results from articles and check whether the reported p-value matches the accompanying test statistic and degrees of freedom. A previous study showed high interrater reliabilities (between .76 and .89) between statcheck and manual coding of inconsistencies (.76 - .89; Nuijten, Hartgerink, Van Assen, Epskamp, & Wicherts, 2016). Here we present an additional, detailed study of the validity of statcheck. In Study 1, we calculated its sensitivity and specificity. We found that statcheck’s sensitivity (true positive rate) and specificity (true negative rate) were high: between 85.3% and 100%, and between 96.0% and 100%, respectively, depending on the assumptions and settings. The overall accuracy of statcheck ranged from 96.2% to 99.9%. In Study 2, we investigated statcheck’s ability to deal with statistical corrections for multiple testing or violations of assumptions in articles. We found that the prevalence of corrections for multiple testing or violations of assumptions in psychology was higher than we initially estimated in Nuijten et al. (2016). Although we found numerous reporting inconsistencies in results corrected for violations of the sphericity assumption, we demonstrate that inconsistencies associated with statistical corrections are not what is causing the high estimates of the prevalence of statistical reporting inconsistencies in psychology.

scholarly journals Spectral features and optimal Hierarchical Attention Networks for Pulmonary abnormality detection from the Respiratory Sound signals

2021 ◽  
Author(s):  
JAWAD AHMAD DAR ◽  
sajaad Ahmad lone ◽  
Kamal Kr Srivast
Keyword(s):  
Water Cycle ◽  
Optimization Technique ◽  
True Positive Rate ◽  
Respiratory Sounds ◽  
Training Procedure ◽  
True Negative ◽  
Important Concern ◽  
Efficient Performance ◽  
Positive Rate ◽  
Pulmonary Abnormalities

Abstract The most important concern in the medical field is to consider the analysis of data and perform accurate diagnosis. However, the analysis of pulmonary abnormalities may depend on the diagnostic experience and the medical skills of the physicians, and is a time-consuming practice. In order to solve such issues, an efficient Water Cycle Swarm Optimizer-based Hierarchical Attention Network (WCSO-based HAN) is developed for detecting the pulmonary abnormalities from the respiratory sounds signals. However, the developed optimization technique named WCSO is devised by incorporating the Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). Here, the pre-processing is performed using the Hanning window and Spectral gating-based noise reduction method in order to remove the falsifications or noises from the signal. Thereafter, the process of feature extraction is carried out to extract the significant features, such as Bark frequency Cepstral coefficient (BFCC) and the short term features, such asspectral flux and spectral centroid. Once the significant features are extracted, classification is performed using HAN where the training procedure of HAN is carried out using WCSO. Furthermore, the developed WCSO-based HAN obtained efficient performance using True Positive Rate (TPR), True Negative Rate (TNR) and accuracy with the values of 0.943, 0.913, and 0.923 using dataset 1, respectively.

scholarly journals genuMet: distinguish genuine untargeted metabolic features without quality control samples

2019 ◽  
Author(s):  
L Cao ◽  
C Clish ◽  
FB Hu ◽  
MA Martínez-González ◽  
C Razquin ◽  
...  
Keyword(s):  
Quality Control ◽  
Large Scale ◽  
Control Method ◽  
R Package ◽  
True Positive Rate ◽  
Supplementary Information ◽  
True Negative ◽  
True Negative Rate ◽  
Positive Rate ◽  
Measurement Artifacts

AbstractMotivationLarge-scale untargeted metabolomics experiments lead to detection of thousands of novel metabolic features as well as false positive artifacts. With the incorporation of pooled QC samples and corresponding bioinformatics algorithms, those measurement artifacts can be well quality controlled. However, it is impracticable for all the studies to apply such experimental design.ResultsWe introduce a post-alignment quality control method called genuMet, which is solely based on injection order of biological samples to identify potential false metabolic features. In terms of the missing pattern of metabolic signals, genuMet can reach over 95% true negative rate and 85% true positive rate with suitable parameters, compared with the algorithm utilizing pooled QC samples. genu-Met makes it possible for studies without pooled QC samples to reduce false metabolic signals and perform robust statistical analysis.Availability and implementationgenuMet is implemented in a R package and available on https://github.com/liucaomics/genuMet under GPL-v2 license.ContactLiming Liang: [email protected] informationSupplementary data are available at ….

scholarly journals Online asynchronous decoding of error-related potentials during the continuous control of a robot

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Catarina Lopes-Dias ◽  
Andreea I. Sburlea ◽  
Gernot R. Müller-Putz
Keyword(s):  
True Positive Rate ◽  
Robotic Arm ◽  
Continuous Control ◽  
True Positive ◽  
True Negative ◽  
Computer Interfaces ◽  
True Negative Rate ◽  
Positive Rate ◽  
Related Potentials ◽  
Neural Signature

AbstractError-related potentials (ErrPs) are the neural signature of error processing. Therefore, the detection of ErrPs is an intuitive approach to improve the performance of brain-computer interfaces (BCIs). The incorporation of ErrPs in discrete BCIs is well established but the study of asynchronous detection of ErrPs is still in its early stages. Here we show the feasibility of asynchronously decoding ErrPs in an online scenario. For that, we measured EEG in 15 participants while they controlled a robotic arm towards a target using their right hand. In 30% of the trials, the control of the robotic arm was halted at an unexpected moment (error onset) in order to trigger error-related potentials. When an ErrP was detected after the error onset, participants regained the control of the robot and could finish the trial. Regarding the asynchronous classification in the online scenario, we obtained an average true positive rate (TPR) of 70% and an average true negative rate (TNR) of 86.8%. These results indicate that the online asynchronous decoding of ErrPs was, on average, reliable, showing the feasibility of the asynchronous decoding of ErrPs in an online scenario.

Mastoid, middle ear and inner ear analysis in CT scan – a possible contribution for the identification of remains

2020 ◽  
Vol 60 (2) ◽  
pp. 102-111
Author(s):  
Henrique Rodrigues ◽  
Rosa Ramos ◽  
Leoni Fagundes ◽  
Orlando Galego ◽  
David Navega ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Visual Assessment ◽  
True Positive Rate ◽  
Observer Agreement ◽  
True Positive ◽  
True Negative ◽  
True Negative Rate ◽  
Negative Rate ◽  
Positive Rate

Objective We aimed to evaluate whether the internal structures of the human ear have anatomical characteristics that are sufficiently distinctive to contribute to human identification and use in a forensic context. Materials and methods After data anonymisation, a dataset containing temporal bone CT scans of 100 subjects was processed by a radiologist who was not involved in the study. Four reference images were selected for each subject. Of the original sample, 10 examinations were used for visual comparison, case by case, against the dataset of 100 patients. This visual assessment was performed independently by four observers, who evaluated the anatomical agreement using a Likert scale (1–5). Inter-observer agreement, true positive rate, positive predictive value, true negative rate, negative predictive value, false positive rate, false negative rate and positive likelihood ratio (LR+) were evaluated. Results Inter-observer agreement obtained an overall Cohen’s Kappa = 99.59%. True positive rate, positive predictive value, true negative rate and negative predictive value were all 100%. Conclusion Visual assessment of the mastoid examinations was shown to be a robust and reliable approach to identify unique osseous features and contribute to human identification. The statistical analysis indicates that regardless of the examiner’s background and training, the approach has a high degree of accuracy.

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