PMID- 22952789
OWN - NLM
STAT- MEDLINE
DCOM- 20130219
LR  - 20211021
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 7
IP  - 8
DP  - 2012
TI  - Use of artificial intelligence to shorten the behavioral diagnosis of autism.
PG  - e43855
LID - 10.1371/journal.pone.0043855 [doi]
LID - e43855
AB  - The Autism Diagnostic Interview-Revised (ADI-R) is one of the most commonly used 
      instruments for assisting in the behavioral diagnosis of autism. The exam 
      consists of 93 questions that must be answered by a care provider within a 
      focused session that often spans 2.5 hours. We used machine learning techniques 
      to study the complete sets of answers to the ADI-R available at the Autism 
      Genetic Research Exchange (AGRE) for 891 individuals diagnosed with autism and 75 
      individuals who did not meet the criteria for an autism diagnosis. Our analysis 
      showed that 7 of the 93 items contained in the ADI-R were sufficient to classify 
      autism with 99.9% statistical accuracy. We further tested the accuracy of this 
      7-question classifier against complete sets of answers from two independent 
      sources, a collection of 1654 individuals with autism from the Simons Foundation 
      and a collection of 322 individuals with autism from the Boston Autism 
      Consortium. In both cases, our classifier performed with nearly 100% statistical 
      accuracy, properly categorizing all but one of the individuals from these two 
      resources who previously had been diagnosed with autism through the standard 
      ADI-R. Our ability to measure specificity was limited by the small numbers of 
      non-spectrum cases in the research data used, however, both real and simulated 
      data demonstrated a range in specificity from 99% to 93.8%. With incidence rates 
      rising, the capacity to diagnose autism quickly and effectively requires careful 
      design of behavioral assessment methods. Ours is an initial attempt to 
      retrospectively analyze large data repositories to derive an accurate, but 
      significantly abbreviated approach that may be used for rapid detection and 
      clinical prioritization of individuals likely to have an autism spectrum 
      disorder. Such a tool could assist in streamlining the clinical diagnostic 
      process overall, leading to faster screening and earlier treatment of individuals 
      with autism.
FAU - Wall, Dennis P
AU  - Wall DP
AD  - Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, 
      United States of America. dpwall@hms.harvard.edu
FAU - Dally, Rebecca
AU  - Dally R
FAU - Luyster, Rhiannon
AU  - Luyster R
FAU - Jung, Jae-Yoon
AU  - Jung JY
FAU - Deluca, Todd F
AU  - Deluca TF
LA  - eng
GR  - R01 MH090611/MH/NIMH NIH HHS/United States
GR  - 1R01MH090611-01A1/MH/NIMH NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20120827
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - Adolescent
MH  - Adult
MH  - *Artificial Intelligence
MH  - Autistic Disorder/*diagnosis
MH  - *Behavior
MH  - Case-Control Studies
MH  - Child
MH  - Child, Preschool
MH  - Decision Trees
MH  - Humans
MH  - Infant
MH  - Infant, Newborn
MH  - Middle Aged
MH  - Retrospective Studies
MH  - Surveys and Questionnaires
MH  - Time Factors
MH  - Young Adult
PMC - PMC3428277
COIS- Competing Interests: The authors have declared that no competing interests exist.
EDAT- 2012/09/07 06:00
MHDA- 2013/02/21 06:00
PMCR- 2012/08/27
CRDT- 2012/09/07 06:00
PHST- 2011/07/15 00:00 [received]
PHST- 2012/07/26 00:00 [accepted]
PHST- 2012/09/07 06:00 [entrez]
PHST- 2012/09/07 06:00 [pubmed]
PHST- 2013/02/21 06:00 [medline]
PHST- 2012/08/27 00:00 [pmc-release]
AID - PONE-D-11-13905 [pii]
AID - 10.1371/journal.pone.0043855 [doi]
PST - ppublish
SO  - PLoS One. 2012;7(8):e43855. doi: 10.1371/journal.pone.0043855. Epub 2012 Aug 27.