Early Childhood Anxiety and Depression Detection Based on Speech Using Machine Learning Analysis

2021 ◽  
pp. 451-460
Author(s):  
Suvarna Nandyal ◽  
P. S. Swathi
Keyword(s):  
Machine Learning ◽  
Early Childhood ◽  
Childhood Anxiety ◽  
Anxiety And Depression ◽  
Depression Detection ◽  
Learning Analysis

scholarly journals Giving Voice to Vulnerable Children: Machine Learning Analysis of Speech Detects Anxiety and Depression in Early Childhood

2019 ◽  
Vol 23 (6) ◽  
pp. 2294-2301 ◽  
Author(s):  
Ellen W. McGinnis ◽  
Steven P. Anderau ◽  
Jessica Hruschak ◽  
Reed D. Gurchiek ◽  
Nestor L. Lopez-Duran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Childhood ◽  
Anxiety And Depression ◽  
Vulnerable Children ◽  
Learning Analysis

scholarly journals fMRI-Based Machine Learning Analysis of Neural Substrates of Pediatric Anxiety: Temporal Pole and Emotional Face-Responses.

2020 ◽  
Author(s):  
Jeffrey Sawalha ◽  
Muhammad Yousefnezhad ◽  
Alessandro Selvitella ◽  
Bo Cao ◽  
Andrew Greenshaw ◽  
...  
Keyword(s):  
Machine Learning ◽  
Separation Anxiety ◽  
Childhood Anxiety ◽  
Fearful Faces ◽  
Pediatric Anxiety ◽  
Machine Learning Model ◽  
Temporal Pole ◽  
The Right ◽  
Emotional Face ◽  
Learning Analysis

Abstract A prominent cognitive aspect of anxiety is dysregulation of emotional interpretation of facial expressions, associated with neural activity from the amygdala and prefrontal cortex. We report machine learning analysis of fMRI results supporting a key role for a third area, the temporal pole (TP) for childhood anxiety in this context. This finding is based on differential fMRI responses to emotional faces (e.g. angry versus fearful faces) in children with one or more of generalized anxiety, separation anxiety, and social phobia (n = 22) compared with matched controls (n = 23). In our machine learning model, the right TP distinguished anxious from control children (accuracy = 81%). Involvement of the TP as significant for neurocognitive aspects of pediatric anxiety is a novel finding worthy of further investigation.

scholarly journals Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor type

2021 ◽  
Vol 14 (3) ◽  
pp. 101016 ◽  
Author(s):  
Jim Abraham ◽  
Amy B. Heimberger ◽  
John Marshall ◽  
Elisabeth Heath ◽  
Joseph Drabick ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tumor Type ◽  
Learning Analysis

scholarly journals Technical Note: A Guide to Annotation of Neurosurgical Intraoperative Video for Machine Learning Analysis and Computer Vision

2021 ◽  
Author(s):  
Dhiraj J. Pangal ◽  
Guillaume Kugener ◽  
Shane Shahrestani ◽  
Frank Attenello ◽  
Gabriel Zada ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Technical Note ◽  
Learning Analysis

Depression detection from sMRI and rs-fMRI images using machine learning

2021 ◽  
Author(s):  
Marzieh Mousavian ◽  
Jianhua Chen ◽  
Zachary Traylor ◽  
Steven Greening
Keyword(s):  
Machine Learning ◽  
Depression Detection

Machine learning analysis of multispectral imaging and clinical risk factors to predict amputation wound healing

2021 ◽  
Author(s):  
John J. Squiers ◽  
Jeffrey E. Thatcher ◽  
David Bastawros ◽  
Andrew J. Applewhite ◽  
Ronald D. Baxter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Wound Healing ◽  
Multispectral Imaging ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Learning Analysis
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