부산대학교 도서관

Existing screening tools for early detection of autism are expensive, cumbersome, time-intensive, and sometimes fall short in predictive value. In this work, we apply Machine Learning to gold standard clinical data obtained across thousands of children at risk for autism spectrum disorders to create a low-cost, quick, and easy to apply autism screening tool that performs better than most widely used standardized instruments. This new tool combines two screening methods into a single assessment, one based on short, structured parent-reported questionnaires and the other based on tagging key behaviors from short, semi-structured home videos of children. We demonstrate a significant accuracy improvement over standard screening tools in a clinical study sample of 162 children. We further discuss the challenge of extending machine learning algorithms to conditions beyond autism, and we propose a generalized framework for using machine learning algorithms to simultaneously search for the presence of many different conditions.