A machine learning approach to optimize the assessment of depressive symptomatology
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.
SFI Centre for Research Training in Foundations of Data Science
Science Foundation IrelandFind out more...
Cluster randomised controlled trial (RCT) for late life depression in socioeconomically deprived areas of São Paulo, Brazil (PROACTIVE)
Medical Research CouncilFind out more...
PublicationProcedia Computer Science, 2022, 206, pp. 111-120
Other Funding informationThis work has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number 18/CRT/6049 (E.M). This study was funded by São Paulo Research Foundation (FAPESP, process number 2017/50094‐2) and the Joint Global Health Trials initiative jointly funded by Medical Research Council, Wellcome Trust, and the UK Department for International Development (MRC, process number MR/R006229/1). CAN is supported by FAPESP (2018/19343-9) and MS by CNPq-Brazil (307579/2-19-0).
Also affiliated with
- Health Research Institute (HRI)
Sustainable development goals
- (4) Quality Education
Department or School
- Electronic & Computer Engineering