Physiological changes recorded by a wearable biosensor and analyzed through a machine-learning approach can help predict aggressive behavior before it occurs in young psychiatric facility patients with autism, new research shows.
The study published in JAMA Network Open last month by Northeastern University researchers adds to research examining whether imminent aggressive behavior among autistic inpatients can be determined via a wearable biosensor and machine learning.
About one in 36 children were diagnosed with autism spectrum disorder (ASD) in 2020, up from one in 44 in 2018, according to the Centers for Disease Control and Prevention’s (CDC) Autism and Developmental Disabilities Monitoring (ADDM) Network. The prevalence of aggression among children and adolescents with ASD is high, with parents reporting in a 2011 study that 68 percent had demonstrated aggression to a caregiver and 49 percent to non-caregivers.
Prior research work by the Northeastern University team showed that three minutes of wearable biosensor-recorded peripheral physiological and motion signals gathered from 20 youths with autism could predict aggression toward others one minute before it occurred using ridge-regularized logistic regression.
The new study aimed to extend that research to determine whether the recorded data could be used to predict aggression toward others even earlier.
Read more at MHealthIntelligence.com.
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