Machine learning in mental health: a scoping review of methods and applications
BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big
data applications for mental health, highlighting current research and applications in …
data applications for mental health, highlighting current research and applications in …
[HTML][HTML] Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges
Recent years have seen the rapid proliferation of clinical prediction models aiming to
support risk stratification and individualized care within psychiatry. Despite growing interest …
support risk stratification and individualized care within psychiatry. Despite growing interest …
The promise of machine learning in predicting treatment outcomes in psychiatry
For many years, psychiatrists have tried to understand factors involved in response to
medications or psychotherapies, in order to personalize their treatment choices. There is …
medications or psychotherapies, in order to personalize their treatment choices. There is …
[HTML][HTML] Machine learning and natural language processing in mental health: systematic review
A Le Glaz, Y Haralambous, DH Kim-Dufor… - Journal of medical …, 2021 - jmir.org
Background Machine learning systems are part of the field of artificial intelligence that
automatically learn models from data to make better decisions. Natural language processing …
automatically learn models from data to make better decisions. Natural language processing …
[HTML][HTML] Natural language processing for mental health interventions: a systematic review and research framework
Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack
of objective outcomes and fidelity metrics. AI technologies and specifically Natural …
of objective outcomes and fidelity metrics. AI technologies and specifically Natural …
A scoping review of machine learning in psychotherapy research
Abstract Machine learning (ML) offers robust statistical and probabilistic techniques that can
help to make sense of large amounts of data. This scoping review paper aims to broadly …
help to make sense of large amounts of data. This scoping review paper aims to broadly …
[HTML][HTML] Methodological and quality flaws in the use of artificial intelligence in mental health research: systematic review
R Tornero-Costa, A Martinez-Millana… - JMIR Mental …, 2023 - mental.jmir.org
Background: Artificial intelligence (AI) is giving rise to a revolution in medicine and health
care. Mental health conditions are highly prevalent in many countries, and the COVID-19 …
care. Mental health conditions are highly prevalent in many countries, and the COVID-19 …
Introduction to the special section: convergence of automation technology, biomedical engineering, and health informatics toward the healthcare 4.0
Industry 4.0 is spilling out from manufacturing to healthcare. In this article, we provide a brief
history and key enabling technologies of Industry 4.0, and its revolution in healthcare …
history and key enabling technologies of Industry 4.0, and its revolution in healthcare …
Evaluation of clustering and topic modeling methods over health-related tweets and emails
Background Internet provides different tools for communicating with patients, such as social
media (eg, Twitter) and email platforms. These platforms provided new data sources to shed …
media (eg, Twitter) and email platforms. These platforms provided new data sources to shed …
Digital interventions for mental disorders: key features, efficacy, and potential for artificial intelligence applications
Mental disorders are highly prevalent and often remain untreated. Many limitations of
conventional face-to-face psychological interventions could potentially be overcome through …
conventional face-to-face psychological interventions could potentially be overcome through …