[HTML][HTML] A survey of multimodal information fusion for smart healthcare: Mapping the journey from data to wisdom
Multimodal medical data fusion has emerged as a transformative approach in smart
healthcare, enabling a comprehensive understanding of patient health and personalized …
healthcare, enabling a comprehensive understanding of patient health and personalized …
Deep learning for depression recognition with audiovisual cues: A review
With the acceleration of the pace of work and life, people are facing more and more
pressure, which increases the probability of suffering from depression. However, many …
pressure, which increases the probability of suffering from depression. However, many …
Exploring self-attention graph pooling with EEG-based topological structure and soft label for depression detection
T Chen, Y Guo, S Hao, R Hong - IEEE transactions on affective …, 2022 - ieeexplore.ieee.org
Electroencephalogram (EEG) has been widely used in neurological disease detection, ie,
major depressive disorder (MDD). Recently, some deep EEG-based MDD detection …
major depressive disorder (MDD). Recently, some deep EEG-based MDD detection …
Deep multi-modal network based automated depression severity estimation
Depression is a severe mental illness that impairs a person's capacity to function normally in
personal and professional life. The assessment of depression usually requires a …
personal and professional life. The assessment of depression usually requires a …
Integrating deep facial priors into landmarks for privacy preserving multimodal depression recognition
Automatic depression diagnosis is a challenging problem, that requires integrating spatial-
temporal information and extracting features from audio-visual signals. In terms of privacy …
temporal information and extracting features from audio-visual signals. In terms of privacy …
Depression detection on online social network with multivariate time series feature of user depressive symptoms
In recent years, depression has attracted worldwide attention because of its prevalence and
great risk for suicide. Existing studies have confirmed the feasibility of depression detection …
great risk for suicide. Existing studies have confirmed the feasibility of depression detection …
Is speech the new blood? recent progress in ai-based disease detection from audio in a nutshell
In recent years, advancements in the field of artificial intelligence (AI) have impacted several
areas of research and application. Besides more prominent examples like self-driving cars …
areas of research and application. Besides more prominent examples like self-driving cars …
[HTML][HTML] Explainability meets uncertainty quantification: Insights from feature-based model fusion on multimodal time series
Feature importance evaluation is one of the prevalent approaches to interpreting Machine
Learning (ML) models. A drawback of using these methods for high-dimensional datasets is …
Learning (ML) models. A drawback of using these methods for high-dimensional datasets is …
[HTML][HTML] Digital phenotyping for differential diagnosis of major depressive episode: narrative review
E Ettore, P Müller, J Hinze, M Riemenschneider… - JMIR mental …, 2023 - mental.jmir.org
Background Major depressive episode (MDE) is a common clinical syndrome. It can be
found in different pathologies such as major depressive disorder (MDD), bipolar disorder …
found in different pathologies such as major depressive disorder (MDD), bipolar disorder …
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 …