Migliorare l’assistenza per la salute mentale con la fenotipizzazione digitale: raggruppamento dei comportamentit dei pazienti per il supporto decisionale personalizzato

Recenti Prog Med. 2025 Oct;116(10):567-568. doi: 10.1701/4573.45778.
[Article in Italian]

Abstract

Breakthrough digital phenotyping approach reveals three distinct behavioral patterns from smartphone data that could revolutionize personalized mental health care. Using AI clustering on 77 users, we discovered "Night Owls", "Routine-Oriented", and "Always-Connected" behavioral types with 90%+ accuracy. Our explainable ML pipeline identifies key digital biomarkers for targeted interventions, offering clinicians data-driven insights for precision psychiatry.

Publication types

  • English Abstract

MeSH terms

  • Artificial Intelligence
  • Decision Support Systems, Clinical*
  • Humans
  • Mental Disorders* / therapy
  • Mental Health
  • Mental Health Services* / organization & administration
  • Phenotype
  • Precision Medicine* / methods
  • Smartphone