A deep learning probability model to deliver feedback-informed, internet-delivered psychotherapy for depression and anxiety

2021 ◽  
Author(s):  
Jorge Palacios
Keyword(s):  
Deep Learning ◽  
Probability Model ◽  
Depression And Anxiety ◽  
Learning Probability

scholarly journals Augmenting Interpretation of Chest Radiographs With Deep Learning Probability Maps

2020 ◽  
Vol 35 (5) ◽  
pp. 285-293 ◽  
Author(s):  
Brian Hurt ◽  
Andrew Yen ◽  
Seth Kligerman ◽  
Albert Hsiao
Keyword(s):  
Deep Learning ◽  
Chest Radiographs ◽  
Probability Maps ◽  
Learning Probability

scholarly journals Construction of Hybrid Deep Learning Model for Predicting Children Behavior based on their Emotional Reaction

2021 ◽  
Vol 3 (1) ◽  
pp. 29-43
Author(s):  
Senthil Kumar T
Keyword(s):  
Deep Learning ◽  
Decision Tree ◽  
Naive Bayes ◽  
Probability Model ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Children's Behavior ◽  
Behavior Based ◽  
Combined Algorithm ◽  
Naive Bayes Method

Emotion prediction, the sub-domain of sentiment analysis helps to analyze the emotion. Recently, the prediction of children’s behavior based on their present emotional activities is remaining as a challenging task. Henceforth, the deep learning algorithms are used to support and assist in the process of children’s behavior prediction by considering the emotional features with a good accuracy rate. Besides, this article presents the prediction of children’s behavior based on their emotion with the deep learning classifiers method. To analyze the performance, decision tree and naïve Bayes probability model are compared. Totally, 35 sample emotions are considered in the prediction process of deep learning classifier with a probability model. Furthermore, the hybrid emotions are incorporated in the proposed dataset. The comparison between both the decision tree and the Naïve Bayes method has been performed to predict the children’s emotions after the classification. Based on the probability model of naïve Bayes method and decision tree, naïve Bayes method provides good results in terms of recognition rate and prediction accuracy when compared to the decision tree method. Therefore, a fusion of these two algorithms is proposed for predicting the emotions involved in children’s behavior. This research article includes the combined algorithm mathematical proof of prediction based on the emotion samples. This article discusses about the future scope of the proposal and the obtained prediction results.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Individual Differences and Hemispheric Asymmetry While Processing Emotional Words

2014 ◽  
Vol 35 (3) ◽  
pp. 137-143 ◽  
Author(s):  
Lindsay M. Niccolai ◽  
Thomas Holtgraves
Keyword(s):  
Individual Differences ◽  
Hemispheric Asymmetry ◽  
Past Research ◽  
Decision Task ◽  
Depression And Anxiety ◽  
Hemispheric Differences ◽  
Affective Valence ◽  
Emotional Words ◽  
Emotion Words ◽  
The Right

This research examined differences in the perception of emotion words as a function of individual differences in subclinical levels of depression and anxiety. Participants completed measures of depression and anxiety and performed a lexical decision task for words varying in affective valence (but equated for arousal) that were presented briefly to the right or left visual field. Participants with a lower level of depression demonstrated hemispheric asymmetry with a bias toward words presented to the left hemisphere, but participants with a higher level of depression displayed no hemispheric differences. Participants with a lower level of depression also demonstrated a bias toward positive words, a pattern that did not occur for participants with a higher level of depression. A similar pattern occurred for anxiety. Overall, this study demonstrates how variability in levels of depression and anxiety can influence the perception of emotion words, with patterns that are consistent with past research.

Dimensionality and Reliability Assessment of the Pain Patient Profile Questionnaire

2008 ◽  
Vol 24 (1) ◽  
pp. 22-26 ◽  
Author(s):  
Brian E. McGuire ◽  
Michael J. Hogan ◽  
Todd G. Morrison
Keyword(s):  
Self Report ◽  
Pain Patient ◽  
Fit Indices ◽  
Depression And Anxiety ◽  
Oblique Rotation ◽  
Pain Experience ◽  
Patient Profile ◽  
Adequate Representation ◽  
Pain Patients ◽  
Report Measure

Abstract. Objective: To factor analyze the Pain Patient Profile questionnaire (P3; Tollison & Langley, 1995 ), a self-report measure of emotional distress in respondents with chronic pain. Method: An unweighted least squares factor analysis with oblique rotation was conducted on the P3 scores of 160 pain patients to look for evidence of three distinct factors (i.e., Depression, Anxiety, and Somatization). Results: Fit indices suggested that three distinct factors, accounting for 32.1%, 7.0%, and 5.5% of the shared variance, provided an adequate representation of the data. However, inspection of item groupings revealed that this structure did not map onto the Depression, Anxiety, and Somatization division purportedly represented by the P3. Further, when the analysis was re-run, eliminating items that failed to meet salience criteria, a two-factor solution emerged, with Factor 1 representing a mixture of Depression and Anxiety items and Factor 2 denoting Somatization. Each of these factors correlated significantly with a subsample's assessment of pain intensity. Conclusion: Results were not congruent with the P3's suggested tripartite model of pain experience and indicate that modifications to the scale may be required.

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