Related Experiment Video
Updated: Jul 9, 2025
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
Published on: December 15, 2023
A Prospective Study of an Early Prediction Model of Attention Deficit Hyperactivity Disorder Based on Artificial
Gang Wang1, Wanyue Li1, Shixiong Huang2
1Jinan University, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
Objective:
To explore the relationship between the Parent Symptom Questionnaire (PSQ) and attention deficit hyperactivity disorder (ADHD) in China, and the application value of PSQ questionnaire.
Method:
Two hundred two children aged 3 to 14 years were enrolled in this study. Statistical methods were used to screen characteristic factors and explore the relationship between PSQ items and ADHD. Machine learning algorithms were used to evaluate the clinical application value of PSQ in screening ADHD.
Results:
By Mean-Whitney U test, LASSO regression and decision tree, 44, 24 and 12 items were screened out from PSQ with high correlation with ADHD. Then the above items were classified, and the accuracy reached more than 90%. Moreover, the items of ADHD hyperactivity index of PSQ under artificial intelligence algorithm are different from those of PSQ.
Conclusion:
There are some differences in the items of hyperactivity index between the PSQ and ADHD in China. The artificial intelligence algorithm model of ADHD children based on PSQ scale has a high accuracy.
More Related Videos
10:02Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD
Published on: March 12, 2020
13:09Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol
Published on: April 1, 2018
