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Related Concept Videos

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.

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Related Experiment Video

Updated: Jun 17, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Tailoring screening algorithms for a large health system: Autism screening as a case example.

R Christopher Sheldrick1, J L Hooker2, M Davignon3

  • 1Department of Psychiatry, UMass Chan School of Medicine, Worcester, MA.

Academic Pediatrics
|June 15, 2026
PubMed
Summary
This summary is machine-generated.

This study analyzed electronic medical record data to optimize autism screening algorithms for young children. A recommended algorithm balances sensitivity and predictive value, improving early detection and intervention strategies.

Keywords:
Autismpediatricsprimary carescreening

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Related Experiment Videos

Last Updated: Jun 17, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Pediatric healthcare analytics
  • Autism spectrum disorder screening
  • Health informatics

Background:

  • Healthcare systems face choices when implementing autism screening guidelines, including data sources and threshold setting.
  • Optimizing screening algorithms is crucial for effective early detection and intervention in young children.

Purpose of the Study:

  • To analyze electronic medical record (EMR) data to inform the development of autism screening algorithms.
  • To evaluate different screening algorithm parameters, including data sources and thresholds, for children aged 4 and under.

Main Methods:

  • Retrospective analysis of EMR data from 33,490 families.
  • Simulation of various screening algorithms differing in information used and thresholds.
  • Evaluation of outcomes from population health, specialty care, primary care, and family perspectives.

Main Results:

  • A supported algorithm incorporates all screening information sources.
  • Recommends evaluation for children with ≥50% estimated chance of autism diagnosis by age 4.
  • Suggests active monitoring for children with a 3.3% to <50% estimated probability of autism.

Conclusions:

  • Health systems should utilize EMR data to assess tradeoffs when implementing or refining autism screening protocols.
  • Replication of analyses over time is recommended to account for changes in screening practices and population characteristics.