TY  - GEN
AB  - 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.
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
AU  - MacFarlane, Heather
AU  - Salem, Alexandra C.
AU  - Adams, Joel R.
AU  - Lawley, Grace O.
AU  - Bedrick, Steven
AU  - Dolata, Jill K.
AU  - Fombonne, Eric
DA  - 2021
DO  - 10.6083/0v838127z
DO  - DOI
ID  - 9229
KW  - Natural Language Processing
KW  - Autistic Disorder
KW  - Language
KW  - autism
KW  - automated language measures
KW  - expressive language sampling
KW  - neurodivergent
KW  - disfluency
L1  - https://digitalcollections.ohsu.edu/record/9229/files/MacFarlane-Heather-OHSU-ResearchWeek-2021.pdf
L2  - https://digitalcollections.ohsu.edu/record/9229/files/MacFarlane-Heather-OHSU-ResearchWeek-2021.pdf
L4  - https://digitalcollections.ohsu.edu/record/9229/files/MacFarlane-Heather-OHSU-ResearchWeek-2021.pdf
LA  - eng
LK  - https://digitalcollections.ohsu.edu/record/9229/files/MacFarlane-Heather-OHSU-ResearchWeek-2021.pdf
N2  - 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.
PB  - Oregon Health and Science University
PY  - 2021
T1  - Evaluating atypical language in autism using automated  language measures
TI  - Evaluating atypical language in autism using automated  language measures
UR  - https://digitalcollections.ohsu.edu/record/9229/files/MacFarlane-Heather-OHSU-ResearchWeek-2021.pdf
Y1  - 2021
ER  -