AIMC Topic: Mental Disorders
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Computational framework for detection of subtypes of neuropsychiatric disorders based on DTI-derived anatomical connectivity.
The neuroradiology journal
Oct 1, 2020
Many brain disorders - such as Alzheimer's disease, Parkinson's disease, schizophrenia and autism - are heterogeneous, that is, they may have several subtypes. Traditionally, clinicians have identified subtypes, such as subtypes of psychosis, using c...
Machine learning and natural language processing in psychotherapy research: Alliance as example use case.
Journal of counseling psychology
Jul 1, 2020
Artificial intelligence generally and machine learning specifically have become deeply woven into the lives and technologies of modern life. Machine learning is dramatically changing scientific research and industry and may also hold promise for addr...
Making Sense of Computational Psychiatry.
The international journal of neuropsychopharmacology
May 27, 2020
In psychiatry we often speak of constructing "models." Here we try to make sense of what such a claim might mean, starting with the most fundamental question: "What is (and isn't) a model?" We then discuss, in a concrete measurable sense, what it mea...
Increasing the Clinical Psychiatric Knowledge Base About Pathogenic Copy Number Variation.
The American journal of psychiatry
Mar 1, 2020
Specific copy number variants (CNVs) have been robustly associated with intellectual disability, autism, and schizophrenia. Most of the literature focus has been on documenting the existence of these phenomena. There are few data to guide therapeutic...
Validation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Data.
Journal of Alzheimer's disease : JAD
Jan 1, 2020
BACKGROUND: Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated.
Artificial Intelligence and the Future of Psychiatry.
IEEE pulse
Jan 1, 2020
An estimated 792 million people live with mental health disorders worldwide-more than one in ten people-and this number is expected to grow in the shadow of the Coronavirus disease 2019 (COVID-19) pandemic. Unfortunately, there aren't enough mental h...
"Sorry I Didn't Hear You." The Ethics of Voice Computing and AI in High Risk Mental Health Populations.
AJOB neuroscience
Jan 1, 2020
This article examines the ethical and policy implications of using voice computing and artificial intelligence to screen for mental health conditions in low income and minority populations. Mental health is unequally distributed among these groups, w...
Imputation and characterization of uncoded self-harm in major mental illness using machine learning.
Journal of the American Medical Informatics Association : JAMIA
Jan 1, 2020
OBJECTIVE: We aimed to impute uncoded self-harm in administrative claims data of individuals with major mental illness (MMI), characterize self-harm incidence, and identify factors associated with coding bias.
Prediction of Sex-Specific Suicide Risk Using Machine Learning and Single-Payer Health Care Registry Data From Denmark.
JAMA psychiatry
Jan 1, 2020
IMPORTANCE: Suicide is a public health problem, with multiple causes that are poorly understood. The increased focus on combining health care data with machine-learning approaches in psychiatry may help advance the understanding of suicide risk.
Quantifying the Association Between Psychotherapy Content and Clinical Outcomes Using Deep Learning.
JAMA psychiatry
Jan 1, 2020
IMPORTANCE: Compared with the treatment of physical conditions, the quality of care of mental health disorders remains poor and the rate of improvement in treatment is slow, a primary reason being the lack of objective and systematic methods for meas...
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