scholarly journals Use of Machine Learning for Simplification of Symptom Checklist 90

2021 ◽  
Vol 168 ◽  
pp. S132
Author(s):  
Mingyue Shang ◽  
Zhenxiang Chen ◽  
Xiaoqing Jiang ◽  
Hanchen Xu
Keyword(s):  
Machine Learning ◽  
Symptom Checklist

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning

Identification and Utility of a Short Form of the Pediatric Symptom Checklist-Youth Self-Report (PSC-17-Y)

2020 ◽  
Vol 36 (1) ◽  
pp. 56-64
Author(s):  
Paul Bergmann ◽  
Cara Lucke ◽  
Theresa Nguyen ◽  
Michael Jellinek ◽  
John Michael Murphy
Keyword(s):  
Psychosocial Functioning ◽  
Short Form ◽  
Parent Report ◽  
Self Report ◽  
Symptom Checklist ◽  
Factor Analyses ◽  
Youth Self Report ◽  
Confirmatory Factor Analyses ◽  
Pediatric Symptom Checklist ◽  
Exploratory Factor Analyses

Abstract. The Pediatric Symptom Checklist-Youth self-report (PSC-Y) is a 35-item measure of adolescent psychosocial functioning that uses the same items as the original parent report version of the PSC. Since a briefer (17-item) version of the parent PSC has been validated, this paper explored whether a subset of items could be used to create a brief form of the PSC-Y. Data were collected on more than 19,000 youth who completed the PSC-Y online as a self-screen offered by Mental Health America. Exploratory factor analyses (EFAs) were first conducted to identify and evaluate candidate solutions and their factor structures. Confirmatory factor analyses (CFAs) were then conducted to determine how well the data fit the candidate models. Tests of measurement invariance across gender were conducted on the selected solution. The EFAs and CFAs suggested that a three-factor short form with 17 items is a viable and most parsimonious solution and met criteria for scalar invariance across gender. Since the 17 items used on the parent PSC short form were close to the best fit found for any subsets of items on the PSC-Y, the same items used on the parent PSC-17 are recommended for the PSC-Y short form.

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