Machine and deep learning for longitudinal biomedical data: a review of methods and applications
A Cascarano, J Mur-Petit… - Artificial Intelligence …, 2023 - Springer
Exploiting existing longitudinal data cohorts can bring enormous benefits to the medical
field, as many diseases have a complex and multi-factorial time-course, and start to develop …
field, as many diseases have a complex and multi-factorial time-course, and start to develop …
A scoping review of the clinical application of machine learning in data-driven population segmentation analysis
Objective Data-driven population segmentation is commonly used in clinical settings to
separate the heterogeneous population into multiple relatively homogenous groups with …
separate the heterogeneous population into multiple relatively homogenous groups with …
A model of normality inspired deep learning framework for depression relapse prediction using audiovisual data
A Othmani, AO Zeghina, M Muzammel - Computer Methods and Programs …, 2022 - Elsevier
Abstract Background: Depression (Major Depressive Disorder) is one of the most common
mental illnesses. According to the World Health Organization, more than 300 million people …
mental illnesses. According to the World Health Organization, more than 300 million people …
A multimodal computer-aided diagnostic system for depression relapse prediction using audiovisual cues: A proof of concept
A Othmani, AO Zeghina - Healthcare Analytics, 2022 - Elsevier
Major depressive disorder (MDD), also known as depression, is a common and serious
mental disorder. It is characterized by a high rate of relapse or recurrence where a person …
mental disorder. It is characterized by a high rate of relapse or recurrence where a person …
An Industrial Multi Agent System for real-time distributed collaborative prognostics
Despite increasing interest, real-time prognostics (failure prediction) is still not widespread in
industry due to the difficulties of existing systems to adapt to the dynamic and …
industry due to the difficulties of existing systems to adapt to the dynamic and …
How data science can advance mental health research
Accessibility of powerful computers and availability of so-called big data from a variety of
sources means that data science approaches are becoming pervasive. However, their …
sources means that data science approaches are becoming pervasive. However, their …
A Study on the Relationship between Depression Change Types and Suicide Ideation before and after COVID-19
Background: The purpose of this study is to explore and categorize changes in depression,
and investigate the relationship between suicidal ideations before and after the COVID-19 …
and investigate the relationship between suicidal ideations before and after the COVID-19 …
Data-based decision rules to personalize depression follow-up
Depression is a common mental illness with complex and heterogeneous progression
dynamics. Risk grouping of depression treatment population based on their longitudinal …
dynamics. Risk grouping of depression treatment population based on their longitudinal …
Selective sensing of a heterogeneous population of units with dynamic health conditions
Monitoring a large number of units whose health conditions follow complex dynamic
evolution is a challenging problem in many healthcare and engineering applications. For …
evolution is a challenging problem in many healthcare and engineering applications. For …
Machine learning discovery of longitudinal patterns of depression and suicidal ideation
Background and aim Depression is often accompanied by thoughts of self-harm, which are a
strong predictor of subsequent suicide attempt and suicide death. Few empirical data are …
strong predictor of subsequent suicide attempt and suicide death. Few empirical data are …