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Symptom mapping and personalized care for depression, anxiety and stress: A data-driven AI approach.

Sabrinna DelgadoRose Claudia Batistelli VignolaRenato José SassiPeterson Adriano BelanSidnei Alves de Araújo
Published in: Computers in biology and medicine (2024)
The results achieved in the DM (accuracy ≥92.98 %, sensibility ≥86.02 %, specificity ≥97.32 %, and kappa statistic ≥87.98 %), indicating consistent patterns, along with the results produced by the FIS, demonstrate the potential of the proposed approach to assist health professionals in rapidly predicting symptoms of depression, anxiety, and stress, thereby facilitating outpatient screening and emergency care. Furthermore, it can improve the association of symptoms, referral to specialized care, therapeutic proposals, and even investigations of other diseases unrelated to MD.
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