Clinical Efficacy Evaluation of Psychological Nursing Intervention Combined with Drugs Treatment of Children with ADHD under Artificial Intelligence. (29th March 2022)

• Record Type: Journal Article
• Authors: Guo, Ying; Wang, Jinping; Yan, Shuyan; Sui, Shujie
• Other Names: Garg Deepak Academic Editor.
• Is Part Of: Journal of healthcare engineering. Volume 2022(2022)
• Publication Date: 2022-03-29
• Subjects: Hospital buildings -- Environmental engineering -- Periodicals; Medical technology -- Periodicals; Medical informatics -- Periodicals; 610.28
• DOI: 10.1155/2022/1818693
• Languages: English
• ISSNs: 2040-2295

Abstract

Abstract : ADHD in children is one of the most common neurodevelopmental disorders. It is manifested as inattention, hyperactivity, impulsiveness, and other symptoms that are inconsistent with the developmental level in different occasions, accompanied by functional impairment in social, academic, and occupational aspects. At present, the treatment for children with ADHD is mainly based on psychological nursing intervention combined with drug therapy. Therefore, the actual efficacy evaluation of this treatment regimen is very important. Neural networks are widely used in smart medical care. This work combines artificial intelligence with the evaluation of clinical treatment effects of ADHD children and designs an intelligent model based on neural networks for evaluating the clinical efficacy of psychological nursing intervention combined with drug treatment of children with ADHD. The main research is that, for the evaluation of clinical treatment effect of ADHD in children, this paper proposes a 1D Parallel Multichannel Network (1DPMN), which is a convolutional neural network. The results show that network models can extract different data features through different channels and can achieve high accuracy evaluation of clinical efficacy of ADHD in children. On the basis of the model, performance is improved through the study of Adam optimizer to speed up the model convergence, adopts batch normalization algorithm to improve stability, and uses Dropout to improve the generalization ability of the network. Aiming at the problem of too many parameters, the 1DPMN is optimized through the principle of local sparseness, and the model parameters are greatly reduced.