Deep learning in mental health outcome research: a scoping review

C Su, Z Xu, J Pathak, F Wang - Translational Psychiatry, 2020 - nature.com
Mental illnesses, such as depression, are highly prevalent and have been shown to impact
an individual's physical health. Recently, artificial intelligence (AI) methods have been …

Portable technologies for digital phenotyping of bipolar disorder: A systematic review

LF Saccaro, G Amatori, A Cappelli, R Mazziotti… - Journal of affective …, 2021 - Elsevier
Background Bias-prone psychiatric interviews remain the mainstay of bipolar disorder (BD)
assessment. The development of digital phenotyping promises to improve BD management …

Digital phenotyping using multimodal data

AS Cohen, CR Cox, MD Masucci, TP Le… - Current behavioral …, 2020 - Springer
Abstract Purpose of Review Digital phenotyping involves the quantification of in situ
phenotypes using personal digital devices and holds the potential to dramatically reshape …

[PDF][PDF] Deep learning solution for pathological voice detection using LSTM-based autoencoder hybrid with multi-task learning

KG Dávid Sztahó, TM Gábriel - I14th International Joint …, 2021 - researchgate.net
In this paper, a deep learning approach is introduced to detect pathological voice disorders
from continuous speech. Speech as bio-signal is getting more and more attention as a …

Cell-Coupled Long Short-Term Memory With -Skip Fusion Mechanism for Mood Disorder Detection Through Elicited Audiovisual Features

MH Su, CH Wu, KY Huang… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
In early stages, patients with bipolar disorder are often diagnosed as having unipolar
depression in mood disorder diagnosis. Because the long-term monitoring is limited by the …

Automatic detection of depression from text data: A systematic literacture review

F Magami, LA Digiampietri - … of the XVI Brazilian Symposium on …, 2020 - dl.acm.org
Depression is a mental disorder that affects hundreds of millions of people worldwide, with
potentially serious consequences if left without treatment. Despite that, many people still …

Exploring macroscopic and microscopic fluctuations of elicited facial expressions for mood disorder classification

QB Hong, CH Wu, MH Su… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
In the clinical diagnosis of mood disorder, a large proportion of patients with bipolar disorder
(BD) are misdiagnosed as having unipolar depression (UD). Generally, long-term tracking is …

[PDF][PDF] Detection of Bipolar Disorder Using Machine Learning with MRI.

R Sujatha, K Tejesh, H Krithi, HR Shri - ISIC, 2021 - ceur-ws.org
Bipolar disorder is a mental ailment caused by maximal mood swings with emotional highs
and lows. Nowadays, this has become the most common abnormality related to mental …

Exploring microscopic fluctuation of facial expression for mood disorder classification

MH Su, CH Wu, KY Huang, QB Hong… - … Conference on Orange …, 2017 - ieeexplore.ieee.org
In clinical diagnosis of mood disorder, depression is one of the most common psychiatric
disorders. There are two major types of mood disorders: major depressive disorder (MDD) …

Exploring macroscopic fluctuation of facial expression for mood disorder classification

QB Hong, CH Wu, MH Su… - 2018 First Asian …, 2018 - ieeexplore.ieee.org
In clinical diagnosis of mood disorder, a large portion of bipolar disorder patients (BDs) are
misdiagnosed as unipolar depression (UDs). Clinicians have confirmed that BDs generally …