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 …
Artificial intelligence and machine learningaided drug discovery in central nervous system diseases: Stateofthearts and future directions
Neurological disorders significantly outnumber diseases in other therapeutic areas.
However, developing drugs for central nervous system (CNS) disorders remains the most …
However, developing drugs for central nervous system (CNS) disorders remains the most …
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 …
Illusory generalizability of clinical prediction models
It is widely hoped that statistical models can improve decision-making related to medical
treatments. Because of the cost and scarcity of medical outcomes data, this hope is typically …
treatments. Because of the cost and scarcity of medical outcomes data, this hope is typically …
A review of using machine learning approaches for precision education
H Luan, CC Tsai - Educational Technology & Society, 2021 - JSTOR
In recent years, in the field of education, there has been a clear progressive trend toward
precision education. As a rapidly evolving AI technique, machine learning is viewed as an …
precision education. As a rapidly evolving AI technique, machine learning is viewed as an …
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 …
Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and artificial wisdom
Artificial intelligence (AI) is increasingly employed in health care fields such as oncology,
radiology, and dermatology. However, the use of AI in mental health care and …
radiology, and dermatology. However, the use of AI in mental health care and …
Prediction models of functional outcomes for individuals in the clinical high-risk state for psychosis or with recent-onset depression: a multimodal, multisite machine …
N Koutsouleris, L Kambeitz-Ilankovic… - JAMA …, 2018 - jamanetwork.com
Importance Social and occupational impairments contribute to the burden of psychosis and
depression. There is a need for risk stratification tools to inform personalized functional …
depression. There is a need for risk stratification tools to inform personalized functional …
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 …
The WPA-lancet psychiatry commission on the future of psychiatry
Background This Commission addresses several priority areas for psychiatry over the next
decade, and into the 21st century. These represent challenges and opportunities for the …
decade, and into the 21st century. These represent challenges and opportunities for the …