BERT for Long Documents: A Case Study of Automated ICD Coding

Transformer models have achieved great success across many NLP problems. However, previous studies in automated ICD coding concluded that these models fail to outperform some of the earlier solutions such as CNN-based models. In this paper we challenge this conclusion. We present a simple and scalable method to process long text with the existing transformer models such as BERT. We show that this method significantly improves the previous results reported for transformer models in ICD coding, and is able to outperform one of the prominent CNN-based methods

Examining Safety and Usability of Virtual Reality for Children with Autism Spectrum Disorder (ASD)

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social communication difficulties and the presence of repetitive behaviors and restricted interests. Virtual Reality (VR) systems have highly desirable features for delivering interventions to individuals with ASD. In particular, they can offer high levels of authenticity and realism, which in turn, can improve the ecological validity of interventions. They also provide a relatively inexpensive way to practice and learn skills in a personalized, controlled, and safe setting. There is, however, a significant gap in understanding how VR environments are experienced by individuals with ASD. To address this, the objectives of this study were to (1) evaluate the safety and usability of head-mounted VR for children with ASD, and (2) discover demographic and phenotypic predictors of VR usability and safety in ASD. Ultimately, the findings can inform the design of personalized VR-based interventions for this population.M.A.S

Cross-Diagnosis Structural Correlates of Autistic-Like Social Communication Differences

Social communication differences are seen in autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD), but the brain mechanisms contributing to these differences remain largely unknown. To address this gap, we used a data-driven and diagnosis-agnostic approach to discover brain correlates of social communication differences in ASD, ADHD, and OCD, and subgroups of individuals who share similar patterns of brain-behavior associations. A machine learning pipeline (regression clustering) was used to discover the pattern of association between structural brain measures (volume, surface area, and cortical thickness) and social communication abilities. Participants (n = 416) included children with a diagnosis of ASD (n = 192, age = 12.0[5.6], 19% female), ADHD (n = 109, age = 11.1[4.1], 18% female), or OCD (n = 50, age = 12.3[4.2], 42% female), and typically developing controls (n = 65, age = 11.6[7.1], 48% female). The analyses revealed (1) associations with social communication abilities in distributed cortical and subcortical networks implicated in social behaviors, language, attention, memory, and executive functions, and (2) three data-driven, diagnosis-agnostic subgroups based on the patterns of association in the above networks. Our results suggest that different brain networks may contribute to social communication differences in subgroups that are not diagnosis-specific