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Area of Science:

  • Neurology
  • Data Science
  • Digital Health

Background:

  • Amyotrophic Lateral Sclerosis (ALS) diagnosis is primarily clinical, often involving a significant delay from symptom onset.
  • Early identification of ALS is crucial for timely intervention and management.
  • Internet search engine interactions represent a novel data source for health-related research.

Purpose of the Study:

  • To investigate the potential of using internet search engine queries to identify individuals with ALS.
  • To assess if search query patterns can differentiate ALS patients from healthy controls and those with disease mimics.
  • To evaluate the efficacy of a predictive model in a prospective setting.

Main Methods:

  • Anonymized search data from 285 individuals with diagnosed ALS was analyzed.
  • ALS search data was compared against control groups and individuals with ALS disease mimics.
  • A predictive model was developed and validated using prospective search data.

Main Results:

  • The model successfully distinguished ALS patients from controls with an AUC of 0.81.
  • Significant differences were observed between ALS patients and disease mimics (p < .05).
  • Prospective validation yielded an AUC of 0.74, indicating model effectiveness.

Conclusions:

  • Search engine interactions show promise as a tool for ALS screening.
  • Utilizing search data could potentially reduce diagnostic delays for ALS.
  • Further research into digital footprints for ALS identification is warranted.