Classifying Challenging Behaviors in Autism Spectrum Disorder with Word Embeddings

Abstract

The understanding and treatment of challenging behaviors in individuals with Autism Spectrum Disorder are paramount to enabling the success of behavioral therapy; an essential step in this process is the labeling of challenging behaviors demonstrated in therapy sessions. This paper seeks to add quantitative depth to this otherwise qualitative task of challenging behavior classification. Here we leverage neural document embeddings with Word2Vec to represent clinical notes capturing 1,917 recorded instance of challenging behaviors from therapy sessions conducted by a large autism treatment provider. These embeddings then serve as training data for supervised machine learning algorithms in both binary and multiclass classification tasks to identify challenging behaviors, achieving high classification accuracies ranging from 82.7% to 98.5%. We demonstrate that the semantic queues derived from the language of challenging behavior descriptions, modeled using natural language processing techniques, can be successfully leveraged to extract and identify challenging behaviors from real-world clinical data.