Abstract
The Autism Diagnostic Observation Schedule-Generic (ADOS) is one of the most widely used instruments for behavioral evaluation of autism spectrum disorders. It is composed of four modules, each tailored for a specific group of individuals based on their language and developmental level. On average, a module takes between 30 and 60 min to deliver. We used a series of machine-learning algorithms to study the complete set of scores from Module 1 of the ADOS available at the Autism Genetic Resource Exchange (AGRE) for 612 individuals with a classification of autism and 15 non-spectrum individuals from both AGRE and the Boston Autism Consortium (AC). Our analysis indicated that 8 of the 29 items contained in Module 1 of the ADOS were sufficient to classify autism with 100% accuracy. We further validated the accuracy of this eight-item classifier against complete sets of scores from two independent sources, a collection of 110 individuals with autism from AC and a collection of 336 individuals with autism from the Simons Foundation. In both cases, our classifier performed with nearly 100% sensitivity, correctly classifying all but two of the individuals from these two resources with a diagnosis of autism, and with 94% specificity on a collection of observed and simulated non-spectrum controls. The classifier contained several elements found in the ADOS algorithm, demonstrating high test validity, and also resulted in a quantitative score that measures classification confidence and extremeness of the phenotype. With incidence rates rising, the ability to classify autism effectively and quickly requires careful design of assessment and diagnostic tools. Given the brevity, accuracy and quantitative nature of the classifier, results from this study may prove valuable in the development of mobile tools for preliminary evaluation and clinical prioritization—in particular those focused on assessment of short home videos of children—that speed the pace of initial evaluation and broaden the reach to a significantly larger percentage of the population at risk.
Highlights
Autism has a significant genetic component,[1] it is primarily diagnosed through behavioral characteristics
We found that this improved the accuracy of our classifier when compared with the accuracy of only using the 11 controls alone
Because it was our goal to shorten the exam without appreciable loss of accuracy, we selected the alternating decision tree (ADTree) as the optimum algorithm for further analysis and validation
Summary
Autism has a significant genetic component,[1] it is primarily diagnosed through behavioral characteristics. Diagnosing autism has been formalized with instruments carefully designed to measure impairments indicative of autism in three developmental areas: language and communication, reciprocal social interactions and restricted or stereotypical interests and activities. One of the most widely used instruments is the Autism Diagnostic Observation Schedule-Generic (ADOS).[2] The ADOS consists of a variety of semi-structured activities designed to measure social interaction, communication, play and imaginative use of materials. The exam is divided into four modules, each geared towards a specific group of individuals based on their language and developmental level, ensuring coverage for a wide variety of behavioral manifestations. The administrator will score the individual to determine their ADOS-based diagnosis, increasing the total time from observation through scoring to between 60 and 90 min in length
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