Accurate identification of behaviors is essential for diagnosing developmental disorders such as Autism Spectrum Disorder (ASD). We frame the extraction of behaviors from text as a specialized form of event extraction grounded in the TimeML framework and evaluate two approaches: a pipeline model and an end-to-end model that directly extracts behavior spans from raw text. We introduce two novel datasets: a new clinical annotation of an existing Reddit corpus of parent-authored posts in English and a clinically annotated corpus of German ASD diagnostic interviews. On the English dataset, the end-to-end BERT model achieved an F1 score of 73.4% in behavior classification, outperforming the pipeline models (F1: 66.8% and 53.65%). On the German clinical dataset, the end-to-end model reached an even higher F1 score of 80.1%, again outperforming the pipeline (F1: 78.7%) and approaching the gold-annotated upper bound (F1: 92.9%). These results demonstrate that behavior classification benefits from direct extraction, and that our method generalizes across domains and languages.