Machine learning for early detection of autism (and other conditions) using a parental questionnaire and home video screening.

2017 IEEE International Conference on Big Data. Abstract Existing screening tools for early detection of autism are expensive, cumbersome, time-intensive, and sometimes fall short in predictive value. In this work, we apply Machine Learning to gold standard clinical data obtained across thousands of children at risk for autism spectrum disorders to create a low-cost, quick, and easy to apply autism…

Crowdsourced validation of a machine-learning classification system for autism and ADHD.

Translational Psychiatry. Abstract Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) together affect >10% of the children in the United States, but considerable behavioral overlaps between the two disorders can often complicate differential diagnosis. Currently, there is no screening test designed to differentiate between the two disorders, and with waiting times from initial suspicion to diagnosis upwards of…

Clinical Evaluation of a Novel and Mobile Autism Risk Assessment.

Journal of Autism and Developmental Disorders. Abstract The Mobile Autism Risk Assessment (MARA) is a new, electronically administered, 7-question autism spectrum disorder (ASD) screen to triage those at highest risk for ASD. Children 16 months–17 years (N = 222) were screened during their first visit in a developmental-behavioral pediatric clinic. MARA scores were compared to diagnosis from the clinical encounter. Participant median age was…

A Wearable Social Interaction Aid for Children with Autism.

In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. Abstract Over 1 million children under the age of 17 in the US have been identified with Autism Spectrum Disorder (ASD). These children struggle to recognize facial expressions, make eye contact, and engage in social interactions. Gaining these skills requires intensive behavioral interventions that are…

Use of machine learning for behavioral distinction of autism and ADHD

Translational Psychiatry volume 6, page 732(2016) M Duda, R Ma, N Haber & D P Wall Abstract Although autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) continue to rise in prevalence, together affecting >10% of today’s pediatric population, the methods of diagnosis remain subjective, cumbersome and time intensive. With gaps upward of a year between initial suspicion and diagnosis, valuable time where treatments and behavioral…

Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning.

Translational Psychiatry. Abstract Although the prevalence of autism spectrum disorder (ASD) has risen sharply in the last few years reaching 1 in 68, the average age of diagnosis in the United States remains close to 4—well past the developmental window when early intervention has the largest gains. This emphasizes the importance of developing accurate methods to detect risk faster than…

Use of Artificial Intelligence to Shorten the Behavioral Diagnosis of Autism.

PLOS ONE. Abstract 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…

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

Translational Psychiatry. 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…

Location

Social