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A review of machine learning and deep learning approaches on mental health diagnosis
Combating mental illnesses such as depression and anxiety has become a global concern.
As a result of the necessity for finding effective ways to battle these problems, machine …
As a result of the necessity for finding effective ways to battle these problems, machine …
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 …
Machine learning for multimodal mental health detection: a systematic review of passive sensing approaches
As mental health (MH) disorders become increasingly prevalent, their multifaceted
symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing …
symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing …
Automatic depression recognition by intelligent speech signal processing: A systematic survey
Depression has become one of the most common mental illnesses in the world. For better
prediction and diagnosis, methods of automatic depression recognition based on speech …
prediction and diagnosis, methods of automatic depression recognition based on speech …
AVEC 2018 workshop and challenge: Bipolar disorder and cross-cultural affect recognition
The Audio/Visual Emotion Challenge and Workshop (AVEC 2018)" Bipolar disorder, and
cross-cultural affect recognition''is the eighth competition event aimed at the comparison of …
cross-cultural affect recognition''is the eighth competition event aimed at the comparison of …
Augmented datasheets for speech datasets and ethical decision-making
Speech datasets are crucial for training Speech Language Technologies (SLT); however,
the lack of diversity of the underlying training data can lead to serious limitations in building …
the lack of diversity of the underlying training data can lead to serious limitations in building …
Multimodal deep learning framework for mental disorder recognition
Current methods for mental disorder recognition mostly depend on clinical interviews and
self-reported scores that can be highly subjective. Building an automatic recognition system …
self-reported scores that can be highly subjective. Building an automatic recognition system …
Multimodal temporal machine learning for Bipolar Disorder and Depression Recognition
Mental disorder is a serious public health concern that affects the life of millions of people
throughout the world. Early diagnosis is essential to ensure timely treatment and to improve …
throughout the world. Early diagnosis is essential to ensure timely treatment and to improve …
A multimodal approach for mania level prediction in bipolar disorder
Bipolar disorder is a mental health disorder that causes mood swings that range from
depression to mania. Clinical diagnosis of bipolar disorder is based on patient interviews …
depression to mania. Clinical diagnosis of bipolar disorder is based on patient interviews …
A hybrid model for depression detection with transformer and bi-directional long short-term memory
Failure to diagnose and treat depression in a timely manner causes more than three
hundred million people suffering from this mental health disorder worldwide. Depression, a …
hundred million people suffering from this mental health disorder worldwide. Depression, a …