目的探讨注意缺陷多动障碍(attention-deficit/hyperactivity disorder,ADHD)儿童的认知与功能损害特征。方法选取2019年1月—2020年12月在广西壮族自治区妇幼保健院门诊确诊的ADHD患儿(ADHD组,n=320),采用韦氏儿童智力量表(Wechsler in...目的探讨注意缺陷多动障碍(attention-deficit/hyperactivity disorder,ADHD)儿童的认知与功能损害特征。方法选取2019年1月—2020年12月在广西壮族自治区妇幼保健院门诊确诊的ADHD患儿(ADHD组,n=320),采用韦氏儿童智力量表(Wechsler intelligence scale for children,WISC)第4版(WISC-Ⅳ)、中文版SNAP-Ⅳ评定量表父母版(Chinese version of Swanson Nolan and Pelham,versionⅣscale,parent form,SNAP-Ⅳ)和Weiss功能缺陷量表父母版(Weiss functional impairment rating scale,parent form,WFIRS-P)对儿童的认知特征、ADHD症状和功能损害情况进行评估,以同期接受常规健康体检的同年龄段儿童作为正常对照(对照组,n=120)。结果与对照组比较,ADHD组WISC-Ⅳ中言语理解指数、知觉推理指数、工作记忆指数、加工速度指数及总智商均较低,SNAP-Ⅳ中注意缺陷、多动/冲动、对立违抗项目得分及WFIRS-P中家庭、学习/学校、生活技能、自我观念、社会活动、冒险活动项目得分均较高,差异均有统计学意义(均P<0.05)。在ADHD组,智力水平<90分亚组的SNAP-Ⅳ和WFIRS-P各项目得分均显著高于智力水平≥90分亚组,差异均有统计学意义(均P<0.05)。结论ADHD儿童存在认知相对低下和功能损害情况,且智力水平较低者ADHD核心症状及功能损害更严重。展开更多
New technologies such as artificial intelligence,the internet of things,big data,and cloud computing have changed the overall society and economy,and the medical field particularly has tried to combine traditional exa...New technologies such as artificial intelligence,the internet of things,big data,and cloud computing have changed the overall society and economy,and the medical field particularly has tried to combine traditional examination methods and new technologies.The most remarkable field in medical research is the technology of predicting high dementia risk group using big data and artificial intelligence.This review introduces:(1)the definition,main concepts,and classification of machine learning and overall distinction of it from traditional statistical analysis models;and(2)the latest studies in mental science to detect dementia and predict high-risk groups in order to help competent researchers who are challenging medical artificial intelligence in the field of psychiatry.As a result of reviewing 4 studies that used machine learning to discriminate high-risk groups of dementia,various machine learning algorithms such as boosting model,artificial neural network,and random forest were used for predicting dementia.The development of machine learning algorithms will change primary care by applying advanced machine learning algorithms to detect high dementia risk groups in the future.展开更多
文摘目的探讨注意缺陷多动障碍(attention-deficit/hyperactivity disorder,ADHD)儿童的认知与功能损害特征。方法选取2019年1月—2020年12月在广西壮族自治区妇幼保健院门诊确诊的ADHD患儿(ADHD组,n=320),采用韦氏儿童智力量表(Wechsler intelligence scale for children,WISC)第4版(WISC-Ⅳ)、中文版SNAP-Ⅳ评定量表父母版(Chinese version of Swanson Nolan and Pelham,versionⅣscale,parent form,SNAP-Ⅳ)和Weiss功能缺陷量表父母版(Weiss functional impairment rating scale,parent form,WFIRS-P)对儿童的认知特征、ADHD症状和功能损害情况进行评估,以同期接受常规健康体检的同年龄段儿童作为正常对照(对照组,n=120)。结果与对照组比较,ADHD组WISC-Ⅳ中言语理解指数、知觉推理指数、工作记忆指数、加工速度指数及总智商均较低,SNAP-Ⅳ中注意缺陷、多动/冲动、对立违抗项目得分及WFIRS-P中家庭、学习/学校、生活技能、自我观念、社会活动、冒险活动项目得分均较高,差异均有统计学意义(均P<0.05)。在ADHD组,智力水平<90分亚组的SNAP-Ⅳ和WFIRS-P各项目得分均显著高于智力水平≥90分亚组,差异均有统计学意义(均P<0.05)。结论ADHD儿童存在认知相对低下和功能损害情况,且智力水平较低者ADHD核心症状及功能损害更严重。
基金Supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education,No.2018R1D1A1B07041091 and 2021S1A5A8062526.
文摘New technologies such as artificial intelligence,the internet of things,big data,and cloud computing have changed the overall society and economy,and the medical field particularly has tried to combine traditional examination methods and new technologies.The most remarkable field in medical research is the technology of predicting high dementia risk group using big data and artificial intelligence.This review introduces:(1)the definition,main concepts,and classification of machine learning and overall distinction of it from traditional statistical analysis models;and(2)the latest studies in mental science to detect dementia and predict high-risk groups in order to help competent researchers who are challenging medical artificial intelligence in the field of psychiatry.As a result of reviewing 4 studies that used machine learning to discriminate high-risk groups of dementia,various machine learning algorithms such as boosting model,artificial neural network,and random forest were used for predicting dementia.The development of machine learning algorithms will change primary care by applying advanced machine learning algorithms to detect high dementia risk groups in the future.
