Title: “A Step Towards Advancing Digital Phenotyping In Mental Healthcare” Advisor: Antonio Artés Rodríguez. ABSTRACT Smartphones and wrist-wearable devices have penetrated our lives in recent years. According to published statistics, nearly 84% of the world’s population owns a smartphone, and almost 10% own a wearable device today (2022). These devices continuously generate various data sources…
Title: Change-Point Detection Methods for Behavioral Shift Recognition in Mental Healthcare Author: Lorena Romero (GTS) ABSTRACT Human behavior analysis has been approached from different perspectives along time. In recent years, the emergence of new technologies and digitalization advances have risen as an alternative tool for behavior characterization, as well as for the detection of changes over time. In particular,…
Title: “Objective Assessment of Psychiatric Patients via Machine Learning” Advisor: Antonio Artés Rodríguez. ABSTRACT Mental disorders are still a source of not-well-understood human suffering. They affect one out of four people in the world, and they are more costly to treat than cancer and diabetes together. One of the reasons of the high economic burden…
Title: “Probabilistic Models and Natural Language Processing in Health” Advisor: Antonio Artés Rodríguez. ABSTRACT We are living the Artificial Intelligence (AI) era. Whatever we want, we are surrounded by technology, computers, intelligent devices, and incredible machines able to perform more and more complex activities from our daily routine, from a writing predictor to a face…
Title: “Nested filtering methods for Bayesian inference in state space models” Advisor: Joaquín Míguez Arenas. ABSTRACT A common feature to many problems in some of the most active fields of science is the need to calibrate (i.e., estimate the parameters) and then forecast the time evolution of high-dimensional dynamical systems using sequentially collected data. In…
Title Medical Data Wrangling With Sequential Variational Autoencoders Authors D. Barrejón, Pablo M. Olmos and Antonio Artés-Rodríguez Abstract Medical data sets are usually corrupted by noise and missing data. These missing patterns are commonly assumed to be completely random, but in medical scenarios, the reality is that these patterns occur in bursts due to sensors…