Use of machine learning to shorten observation-based screening and diagnosis of autism

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

Introduction to Linked Data and Its Lifecycle on the Web

With linked data, a very pragmatic approach towards achieving the vision of the semantic web has gained some traction in the last years. The term linked data refers to a set of best practices for publishing and interlinking structured data on the web. While many standards, methods and technologies developed within by the semantic web community are applicable for linked data, there are also a number of specific characteristics of linked data, which have to be considered. In this article we introduce the main concepts of linked data. We present an overview of the linked data lifecycle and discuss individual approaches as well as the state-of-the-art with regard to extraction, authoring, linking, enrichment as well as quality of linked data. We conclude the chapter with a discussion of issues, limitations and further research and development challenges of linked data. This article is an updated version of a similar lecture given at reasoning web summer school 2011