Passive sensing data predicts stress in university students: a supervised machine learning method for digital phenotyping

HIGHLIGHTS

  • What: The authors had two central aims: to establish a clear methodological pipeline for processing passive sensing data and extracting features that may be relevant in the context of mental health and_(2) to use this methodology to determine the relationship between patterns of university students` mobility, as indicated by passive sensing data, and their stress levels. Using the AUC metric, the models showed satisfactory performance .
  • Who: L. J. Muhammad and collaborators from the Bayero University Kano, Nigeria have published the paper: Passive sensing data predicts stress in university students: a supervised machine learning method . . .

     

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