Researchers at the University of Chicago have developed a novel computational approach that can reliably predict an eventual diagnosis of autism spectrum disorder (ASD) in young children, without the need for additional blood work or procedures, using only diagnostic codes from past doctor’s visits. The new approach reportedly reduces the number of false positive ASD diagnoses produced by traditional screening methods by half.
ASD can be diagnosed as early as age 2, but false positives flagged by the initial screens traditionally used today can delay the confirmation of a true diagnosis. Given the value of early intervention and the limited number of trained professionals, tools that can potentially reduce the pool of patients required to undergo the lengthy, multistep process to receive an official positive diagnosis can have a profound impact on patient care.
Traditionally, questionnaire-based screening tools are used as the first step in identification of ASD. “But, these are prone to inaccuracies that may arise due to things such as a language barrier or culture barrier, and can give rise to inaccurate diagnoses. By only looking at the data as objectively as possible, our approach avoids some of the pitfalls of traditional screening approaches,” said lead author Dmytro Onishchenko, a senior scientist in the Zero Knowledge Discovery (ZeD) Lab led by Ishanu Chattopadhyay, PhD, Professor of Medicine.
The study, a collaboration between the ZeD Lab and University of Chicago developmental pediatricians Dr. Michael E. Msall, MD, and Dr. Peter J. Smith, MD, was published on October 6 in Science Advances.