Intelligent Recognition of Hospital Image Based on Deep Learning: The Relationship between Adaptive Behavior and Family Function in Children with ADHD.

Hongyi Zhao,Yiqi Lin,Jiangyu Chen,Dr Dilbag Singh

doi:10.1155/2021/4874545

Abstract

Chronic diseases are gradually becoming the main threat to human health. By designing an efficient hospital management platform to quickly identify the corresponding chronic diseases, it can effectively reduce the labor cost, improve the accuracy of disease identification, and improve treatment efficiency. ADHD is a common behavioral disorder in school-age children, and it is also one of the most common chronic health problems in this period. The internationally recognized prevalence of ADHD is 3%–9%. ADHD often brings adverse effects on children's life and studying and at the same time increases difficulties for their families. Therefore, this paper designs an intelligent management platform for public hospitals based on a deep learning algorithm, evaluates the current situation and influencing factors of ADHD children through the child adaptive behavior scale and the family function assessment scale, and designs its intelligent platform by using a new technology of fNIRS. According to the nonlinearity and unsteadiness of the fNIRS signal, this paper proposes a motion noise removal method based on EMD algorithm methods: to automatically identify children with ADHD and improve the cognitive function of children with ADHD by intervention technology. The data are from the outpatients of the Department of Child Psychology of the First People's Hospital of Tianshui City in Gansu Province in 2018. The results showed that there were significant differences in the adaptive behavior scale (CABS) and fad scores between the two groups. In the seven dimensions of family function, there were significant differences between the two groups (P < 0.01). fNIRS management platform can effectively identify ADHD patients with high recognition accuracy. The intelligent management platform can significantly reduce the number of physical examination personnel, prolong the diagnosis and treatment time, reduce a lot of repetitive work, and improve the efficiency of diagnosis and treatment. At the same time, this technology also provides great help for better research and improvement of ADHD patients and provides a reference for the information intelligent construction of modern hospitals.

Highlights

Childhood ADHD is a typical behavior disorder of schoolage children, and it is one of the most common chronic health problems. ey usually show obvious difficulty in concentration, short attention duration, and hyperactivity
Studying and mastering the image recognition method of ADHD patients based on deep learning has a positive impact on improving the problem behavior of ADHD children
Using fNIRS, a new technology to establish a hospital intelligent management platform accurately identifies ADHD patients, intervenes ADHD children’s families, comprehensively and deeply understands the parent-child relationship problems faced by ADHD children and their families, and explores ways to solve the problems. rough fNIRS technology, we can better master the information of patients, reduce the cost of human resources, and accelerate the construction of hospital automation

Summary

Introduction

Childhood ADHD is a typical behavior disorder of schoolage children, and it is one of the most common chronic health problems. ey usually show obvious difficulty in concentration, short attention duration, and hyperactivity. Studying and mastering the image recognition method of ADHD patients based on deep learning has a positive impact on improving the problem behavior of ADHD children. Green J L studied the association of ADHD spectrum disorder symptoms with social function, mental health, quality of life, and sleep in children with and without ADHD. Ere is increasing evidence that the quality of parent relationship is correlated with ADHD symptoms in children. Franke s said the study assessed possible correlations between behavioral profiles, quality of life and perceptions of social support, and parenting styles adopted by 26 mothers of children and adolescents with ADHD diagnosed by the same neurologist. Rough fNIRS technology, we can better master the information of patients, reduce the cost of human resources, and accelerate the construction of hospital automation It has a positive effect on improving the diagnosis mode of disease

Image Recognition Based on Deep Learning

Multilayer Feedforward Network

Investigation of Image Recognition in Children with ADHD

Findings

Analysis of ADHD Patients and Intelligent Management Platform