Machine learning has immense potential to enhance diagnostic and intervention research in the behavioral sciences and may be especially useful in investigations involving the highly prevalent and heterogeneous Azacitidine(Vidaza) syndrome of autism spectrum disorder. studies. We conclude with proposed best-practices when using machine learning in autism research and highlight some especially promising areas for collaborative work at the intersection of computational and behavioral science. clinical content domains. It is essential that a computational researcher consider the sources and properties of the data when applying machine learning techniques; the manner in which data were elicited/collected and what purposes they are intended to serve are of paramount importance to ensure that interpretation of results are accurate unbiased and not overstated. Focusing solely on data control but disregarding context can create misleading results and conclusions. Conversely the application of computational methods by experts outside machine learning areas can be a precarious scenario because there are numerous ways to misuse algorithms and misjudge their results1. As such it is crucial that computational and behavioral experts collaborate in these endeavors with each community learning as much as possible about the other’s website to relay best practices provide context and assist in interpreting results. This approach to inquiry is especially vital in an area with such serious impact and general public health significance as mental health disorders study; if an algorithm is definitely widely purported to Azacitidine(Vidaza) improve diagnostics or aid intervention the claim comes with incredible sociable ramifications and responsibility. The current paper identifies several subtle but important pitfalls when incorporating machine learning techniques in autism diagnostics leading to proposed best-practices for future applications of machine learning in autism study. The impetus for this contribution Azacitidine(Vidaza) stems from two published autism Azacitidine(Vidaza) studies which wanted to use machine learning techniques for very rapid (several minutes) assessment of ASD using the ADOS (Wall et al. 2012 and the Azacitidine(Vidaza) ADI-R (Wall et al. 2012 In addition to critically evaluating these experimental contributions we attempt to reproduce the findings of Wall and colleagues using a larger more balanced corpus of ADOS and ADI-R data while accounting for potential sources of error that we will argue if not tackled produce misleading and non-replicable results. We also recommend a classification overall performance metric called unweighted average recall that is better suited for data with unbalanced classes than the more commonly used measure of overall performance accuracy. Finally we close by briefly outlining applications of machine learning and transmission processing that hold promise to advance our understanding of autism analysis and intervention study. Wall et al. (2012a) Experiments and Critique The experiments of Wall et al. (2012a) claim to shorten the observation-based coding of the ADOS in an effort to provide more time-efficient diagnoses while keeping validity. Our essential analysis of this study begins with a brief overview of the ADOS instrument followed by a description of the data used their experimental statements and our critique citing conceptual and methodological issues in the approach.2 Autism Diagnostic Observation Routine The ADOS is a widely used KLF1 standardized assessment for diagnosing ASD that consists Azacitidine(Vidaza) of 30-45 minutes of semi-structured connection with a trained administrator to elicit and code behaviors relating to social interaction communication play and imaginative use of materials (Lord et al. 2000 The ADOS consists of four unique Modules that vary depending on an individual’s age and verbal capabilities. We focus on Module 1 with this paper as that was the module used in the Wall et al. (2012a) study under examination. It is definitely designed for pre-verbal children and thus is definitely most often utilized for early initial ASD analysis. Module 1 incorporates 29 behavioral codes and 10 subtasks/activities. The ADOS algorithms were designed in an effort to map standardized behavioral observations to an ASD analysis. Creators of the instrument judiciously fused their collective and considerable clinical knowledge Diagnostic and Statistical Manual of Mental Disorders Fourth Release (DSM-IV; American Psychiatric Association 2000 criteria and statistical analytics to handcraft the algorithm (Number 1). The producing algorithm was tested against best-estimate medical (BEC).