Cross-Corpora Unsupervised 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, language impairments may present as delayed speech in a toddler and difficulty reading in a school-age child. 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.
Cite
Text
Elibol et al. "Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders." Journal of Machine Learning Research, 2016.Markdown
[Elibol et al. "Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders." Journal of Machine Learning Research, 2016.](https://mlanthology.org/jmlr/2016/elibol2016jmlr-crosscorpora/)BibTeX
@article{elibol2016jmlr-crosscorpora,
title = {{Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders}},
author = {Elibol, Huseyin Melih and Nguyen, Vincent and Linderman, Scott and Johnson, Matthew and Hashmi, Amna and Doshi-Velez, Finale},
journal = {Journal of Machine Learning Research},
year = {2016},
pages = {1-38},
volume = {17},
url = {https://mlanthology.org/jmlr/2016/elibol2016jmlr-crosscorpora/}
}