Machine learning in mental health: A systematic review of the HCI literature to support the development of effective and implementable ML systems
High prevalence of mental illness and the need for effective mental health care, combined
with recent advances in AI, has led to an increase in explorations of how the field of machine …
with recent advances in AI, has led to an increase in explorations of how the field of machine …
Machine learning algorithms for depression: diagnosis, insights, and research directions
Over the years, stress, anxiety, and modern-day fast-paced lifestyles have had immense
psychological effects on people's minds worldwide. The global technological development …
psychological effects on people's minds worldwide. The global technological development …
[HTML][HTML] Emotion fusion for mental illness detection from social media: A survey
Mental illnesses are one of the most prevalent public health problems worldwide, which
negatively influence people's lives and society's health. With the increasing popularity of …
negatively influence people's lives and society's health. With the increasing popularity of …
[HTML][HTML] Detecting and measuring depression on social media using a machine learning approach: systematic review
D Liu, XL Feng, F Ahmed, M Shahid, J Guo - JMIR Mental Health, 2022 - mental.jmir.org
Background: Detection of depression gained prominence soon after this troublesome
disease emerged as a serious public health concern worldwide. Objective: This systematic …
disease emerged as a serious public health concern worldwide. Objective: This systematic …
[HTML][HTML] Ethics and law in research on algorithmic and data-driven technology in mental health care: scoping review
P Gooding, T Kariotis - JMIR Mental Health, 2021 - mental.jmir.org
Background Uncertainty surrounds the ethical and legal implications of algorithmic and data-
driven technologies in the mental health context, including technologies characterized as …
driven technologies in the mental health context, including technologies characterized as …
Detecting depression of Chinese microblog users via text analysis: Combining Linguistic Inquiry Word Count (LIWC) with culture and suicide related lexicons
S Lyu, X Ren, Y Du, N Zhao - Frontiers in psychiatry, 2023 - frontiersin.org
Introduction In recent years, research has used psycholinguistic features in public discourse,
networking behaviors on social media and profile information to train models for depression …
networking behaviors on social media and profile information to train models for depression …
Prediction of postpartum depression using machine learning techniques from social media text
Early screening of mental disorders plays a crucial role in diagnosis and treatment. This
study explores how data‐driven methods can leverage the information available on social …
study explores how data‐driven methods can leverage the information available on social …
A review on recognizing depression in social networks: challenges and opportunities
Social networks have become another resource for supporting mental health specialists in
making inferences and finding indications of mental disorders, such as depression. This …
making inferences and finding indications of mental disorders, such as depression. This …
Predicting depression symptoms in an Arabic psychological forum
NS Alghamdi, HAH Mahmoud, A Abraham… - IEEE …, 2020 - ieeexplore.ieee.org
Recently, social media platforms have been widely used as a communication tool on social
networks. Many users have utilized these platforms to reflect their personal lives. These …
networks. Many users have utilized these platforms to reflect their personal lives. These …
Exploring the dominant features of social media for depression detection
Recently, social media have been used by researchers to detect depressive symptoms in
individuals using linguistic data from users' posts. In this study, we propose a framework to …
individuals using linguistic data from users' posts. In this study, we propose a framework to …