TY - JOUR AU - Ben-Sasson, Ayelet AU - Yom-Tov, Elad PY - 2016 DA - 2016/11/22 TI -家长对孩子自闭症谱系障碍的在线关注:症状内容分析和风险自动预测JO - J Med Internet Res SP - e300 VL - 18 IS - 11 KW -在线查询KW -自闭症障碍KW -家长KW -机器学习KW -早期检测AB -背景:在线社区被父母用作核实与孩子有关的发展和健康问题的平台。随着公众对自闭症谱系障碍(ASD)认识的提高，越来越多的家长怀疑自己的孩子患有自闭症。ASD的早期识别对于早期干预非常重要。目的:对怀疑孩子可能患有ASD的父母提出的在线查询中提到的症状进行特征描述，并确定这些症状是否具有年龄特异性。为了测试机器学习工具在根据父母的叙述对儿童患自闭症风险进行分类方面的功效。方法:为此，我们分析了担心孩子可能患有ASD的家长提出的在线查询，并根据ASD特异性和非ASD特异性域对他们提到的警告信号进行了分类。然后，我们使用这些数据来测试训练有素的机器学习工具对ASD风险程度进行分类的有效性。雅虎答案(Yahoo Answers)是一个发布问题和寻找答案的社交网站，该网站是针对家长询问社区他们的孩子是否患有自闭症谱系障碍的问题进行挖掘的。本研究共收集了195份问卷(儿童平均年龄=38.0个月; 84.7% [160/189] boys). Content text analysis of the queries aimed to categorize the types of symptoms described and obtain clinical judgment of the child’s ASD-risk level. Results: Concerns related to repetitive and restricted behaviors and interests (RRBI) were the most prevalent (75.4%, 147/195), followed by concerns related to language (61.5%, 120/195) and emotional markers (50.3%, 98/195). Of the 195 queries, 18.5% (36/195) were rated by clinical experts as low-risk, 30.8% (60/195) as medium-risk, and 50.8% (99/195) as high-risk. Risk groups differed significantly (P<.001) in the rate of concerns in the language, social, communication, and RRBI domains. When testing whether an automatic classifier (decision tree) could predict if a query was medium- or high-risk based on the text of the query and the coded symptoms, performance reached an area under the receiver operating curve (ROC) curve of 0.67 (CI 95% 0.50-0.78), whereas predicting from the text and the coded signs resulted in an area under the curve of 0.82 (0.80-0.86). Conclusions: Findings call for health care providers to closely listen to parental ASD-related concerns, as recommended by screening guidelines. They also demonstrate the need for Internet-based screening systems that utilize parents’ narratives using a decision tree questioning method. SN - 1438-8871 UR - //www.mybigtv.com/2016/11/e300/ UR - https://doi.org/10.2196/jmir.5439 UR - http://www.ncbi.nlm.nih.gov/pubmed/27876688 DO - 10.2196/jmir.5439 ID - info:doi/10.2196/jmir.5439 ER -