@article{IR,
      recid = {9229},
      author = {MacFarlane, Heather and Salem, Alexandra C. and Adams,  Joel R. and Lawley, Grace O. and Bedrick, Steven and  Dolata, Jill K. and Fombonne, Eric},
      title = {Evaluating atypical language in autism using automated   language measures},
      publisher = {Oregon Health and Science University},
      address = {2021},
      number = {IR},
      abstract = {Structural and pragmatic language deficits are core  symptoms of Autism Spectrum Disorder (ASD) and predict  long-term outcomes. Clinical measurement of language  proficiency is cumbersome and costly; however, Automated  Language Measures (ALMs) can be automatically calculated  from language samples. 

Objectives:
1. examine language  differences between three clinical groups (ASD, Attention  Deficit Hyperactivity Disorder (ADHD), and Typically  Developing (TD)); 2. analyze the convergent validity of  these measures by calculating correlations between the ALMs  and standardized language measures; 3. investigate the  accuracy of each individual ALM in predicting ASD status;  and 4. investigate any gains in accuracy obtained by  combining all ALMs together to predict ASD status.},
      url = {http://digitalcollections.ohsu.edu/record/9229},
      doi = {https://doi.org/10.6083/0v838127z},
}