TY - JOUR
T1 - A Machine Learning approach to optimize the assessment of depressive symptomatology
AU - Eduardo, Maekawa
AU - Darragh, Glavin
AU - Eoin Martino, Grua
AU - Carina Akemi, Nakamura
AU - Marcia, Scazufca
AU - Ricardo, Araya
AU - Peters Tim, J.
AU - Pepijn, Van De Ven
N1 - Publisher Copyright:
© 2022 The Author(s).
PY - 2022
Y1 - 2022
N2 - In this work, we developed a machine learning model to predict depressive symptomatology (DS). The model achieved an AUC of 0.71 and a sensitivity of 74.8%. When developing the model, we applied a Bayesian network approach to select its predictors. This probabilistic approach facilitates the understanding of the relationships between predictors and DS. In consequence, we were able to identify that having balance problems and experiencing shortness of breath are directly related to DS.
AB - In this work, we developed a machine learning model to predict depressive symptomatology (DS). The model achieved an AUC of 0.71 and a sensitivity of 74.8%. When developing the model, we applied a Bayesian network approach to select its predictors. This probabilistic approach facilitates the understanding of the relationships between predictors and DS. In consequence, we were able to identify that having balance problems and experiencing shortness of breath are directly related to DS.
KW - Bayesian networks
KW - Depressive symptomatology
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85143629626&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2022.09.090
DO - 10.1016/j.procs.2022.09.090
M3 - Conference article
AN - SCOPUS:85143629626
SN - 1877-0509
VL - 206
SP - 111
EP - 120
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 11th Scientific Meeting of the International Society for Research on Internet Interventions, ISRII 2022
Y2 - 18 September 2022 through 22 September 2022
ER -