机器学习在自闭症儿童早期识别和诊断领域的应用

Application of machine learning in early identification and diagnosis of autistic children

早发现、早诊断、早干预是开展自闭症儿童教育康复工作的共识,但传统识别和诊断方法局限及专业人员缺乏常导致自闭症儿童错失最佳干预期。为改善现状,近年来机器学习凭借其客观准确、简便灵活等方面的优势,逐渐被应用到自闭症的早期预测、筛查、诊断和评估过程管理中,积累了较为丰富的成果。但是机器学习也在研究对象选取、分类数据采集和理论模型应用等方面存在局限性。未来研究应推动构建孕产期和新生儿病理生理信息追踪数据库和标准化模型分类指标体系,同时继续优化算法,加快智能化自闭症识别和诊断理论成果向实践转化。

Early detection,early diagnosis and early intervention have been determined as the consensus of autistic children education and rehabilitation.However,both the limitations of traditional identification and diagnosis methods and the lack of professionals mostly caused the miss of the well-timed intervention in autistic children.Considering the advantages of machine learning(i.e.,objectivity,accuracy,simplicity and flexibility),which has been gradually applied in different aspects of autistic education and rehabilitation,including early prediction,screening,diagnosis and evaluation,to improve this situation.Despite that,a series of limitations,such as research objects selection,classified data collection,theoretical model application,still exist.How to promote the construction of the tracking database of maternal and newborn pathophysiological information and establish a standardized model classification index system should be the focus of the future research.At the same time,researchers should pay more attention to the algorithm optimization and accelerate the transformation of the theoretical achievements of intelligent autism identification and diagnosis to practice.