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.
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