Depression Detection
23 papers with code • 4 benchmarks • 5 datasets
Depression Detection is the problem of identifying signs of depression in individuals. These signs might be identified in peoples’ speech, facial expressions and in the use of language.
Benchmarks
These leaderboards are used to track progress in Depression Detection
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Datasets
SMHD
Perla Dataset
Most implemented papers
Gender Bias in Depression Detection Using Audio Features
adbailey1/DepAudioNet_reproduction
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Depression is a large-scale mental health problem and a challenging area for machine learning researchers in detection of depression.
Text-based depression detection on sparse data
richermans/text_based_depression
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Previous text-based depression detection is commonly based on large user-generated data.
A Text Classification Framework for Simple and Effective Early Depression Detection Over Social Media Streams
With the rise of the Internet, there is a growing need to build intelligent systems that are capable of efficiently dealing with early risk detection (ERD) problems on social media, such as early depression detection, early rumor detection or identification of sexual predators.
Cost-sensitive Boosting Pruning Trees for depression detection on Twitter
Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year.
Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviews
danaiksez/depression-detection
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In this work we propose a machine learning model for depression detection from transcribed clinical interviews.
Looking At The Body: Automatic Analysis of Body Gestures and Self-Adaptors in Psychological Distress
Compared to modalities such as face, head, and vocal, research investigating the use of the body modality for these tasks is relatively sparse.
FedMood: Federated Learning on Mobile Health Data for Mood Detection
Depression is one of the most common mental illness problems, and the symptoms shown by patients are not consistent, making it difficult to diagnose in the process of clinical practice and pathological research.
Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model
speechandlanguageprocessing/icassp2022-depression
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Depression is a global mental health problem, the worst case of which can lead to suicide.
Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires
thongnt99/acl22-depression-phq9
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ACL 2022
In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9's symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach.
Integration of Text and Graph-based Features for Detecting Mental Health Disorders from Voice
With the availability of voice-enabled devices such as smart phones, mental health disorders could be detected and treated earlier, particularly post-pandemic.
