Fast identification of depressive symptomatology using probabilistic machine learning

Project Motivation

Depression is often overlooked and this tends to lead to the provision of sub-optimal symptom treatment or a lack of treatment altogether. This can generate an irreversible social and economic impact on society.  Therefore, there is an urgent necessity to elaborate faster, scalable, and low-cost depression identification tools. 

Project Description

To identify people with depressive symptomatology (DS) in a faster way, we aim to apply 3 approaches using probabilistic Machine learning techniques: 

Status: Ongoing

Team Members

Eduardo Maekawa

Darragh Glavin

Prof. Pepijn van de Ven

Dr. Eoin Martino Grua


King’s College London

University of São Paulo

University of Bristol