[gov3009-l] Applied Statistics - 3/2 - Finale Doshi-Velez

Hi everyone! This week at the Applied Statistics Workshop we will be welcoming *Finale Doshi-Velez*, Assistant Professor of Computer Science at Harvard University. She will be presenting work entitled *Cross-Corpora Learning of Trajectories in Autism Spectrum Disorders**.* Please find the abstract below and on the website <http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations/322016-finale-doshi-velez-harvard-title-coming-soon> . As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and lunch will be provided. See you all there! To view previous Applied Statistics presentations, please visit the website <http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>. -- Aaron Kaufman Title: Cross-Corpora Learning of Trajectories in Autism Spectrum Disorders Abstract: Patients with developmental disorders, such as autism spectrum disorder (ASD), present with symptoms that change with time even if the named diagnosis remains fixed. For example, a child may have delayed speech as a toddler and difficulty reading in elementary school. Characterizing these trajectories is important for early treatment. However, deriving these trajectories from observational sources is challenging: electronic health records only reflect observations of patients at irregular intervals and only record what factors are clinically relevant at the time of observation. Meanwhile, caretakers discuss daily developments and concerns on social media. I will present ongoing work on a fully unsupervised approach for learning disease trajectories from incomplete medical records, including records with only a single observation of each patient, combined with disease descriptions from alternate data sources. In particular, we use a dynamic topic model with efficient inference via polya-gamma augmentation. We learn disease trajectories from the electronic health records of 13,435 patients with ASD and the forum posts of 13,743 caretakers of children with ASD, deriving interesting clinical insights as well as good predictions.