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A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
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Predicting eating disorders from Internet activity.

Shiri Sadeh-Sharvit1,2, Ellen E Fitzsimmons-Craft3, C Barr Taylor2,4

  • 1Baruch Ivcher School of Psychology, Interdisciplinary Center, Herzliya, Israel.

The International Journal of Eating Disorders
|July 25, 2020
PubMed
Summary
This summary is machine-generated.

Internet browsing data can help predict eating disorder (ED) risk using machine learning. This technology may enable earlier identification and intervention for individuals with EDs, improving health outcomes.

Keywords:
Internet activitybrowsing historyeating disordersonline screening

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Area of Science:

  • Psychiatry
  • Computer Science
  • Public Health

Background:

  • Eating disorders (EDs) significantly impact health and functioning.
  • Delayed acknowledgment and treatment seeking are common barriers to recovery.
  • Early identification and intervention are crucial public health goals for EDs.

Purpose of the Study:

  • To investigate the potential of Internet activity data for predicting eating disorder (ED) risk and diagnostic status.
  • To explore innovative approaches for early identification of individuals with EDs.
  • To inform timely linkage with evidence-based interventions.

Main Methods:

  • A machine learning algorithm was developed to predict ED status.
  • Data included clinically validated online surveys and Internet browsing history from 936 women.
  • The algorithm analyzed attributes from Internet activity to classify participants into ED risk categories.

Main Results:

  • The machine learning model achieved 52.6% accuracy in predicting ED risk/diagnostic status, outperforming random chance (38.1%).
  • Key predictive variables included ED-related search terms, ED content website visits, age, browsing frequency, and daily activity patterns.
  • The model showed a 38% relative improvement in prediction accuracy.

Conclusions:

  • Internet activity data, analyzed by machine learning, can predict ED risk with moderate accuracy.
  • This approach may help identify individuals needing further assessment for EDs.
  • Future applications could include tailored digital interventions triggered by online behaviors to improve well-being.