An Application of Document Embeddings to Identifying Challenging Behaviors in Autism Spectrum Disorder From Clinical Notes

Labeling challenging behaviors demonstrated in Applied Behavioral Analysis therapy is essential in treating challenging behaviors in individuals with Autism Spectrum Disorder. The understanding and accuracy of labeling these challenging behaviors correlate with the efficacy of behavioral therapy. This paper further improves a quantitative solution from previous work by applying neural document embeddings with Doc2Vec to represent the clinical notes on 1,917 recorded instances of challenging behaviors from therapy sessions. Similarly, we implemented the document embeddings as training data into binary and multiclass classification models, whose accuracies ranged between 82.9% and 99.3%. The results with the new input demonstrated that document neural embeddings provided a more accurate representation of the clinical notes, thus providing a more accurate identification of challenging behaviors.