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Hits 1 – 9 of 9

1
Prediction of Loneliness in Older Adults Using Natural Language Processing: Exploring Sex Differences in Speech.
In: The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry, vol 29, iss 8 (2021)
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2
Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom.
In: Biological psychiatry. Cognitive neuroscience and neuroimaging, vol 6, iss 9 (2021)
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3
Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.
Graham, Sarah A; Lee, Ellen E; Jeste, Dilip V. - : eScholarship, University of California, 2020
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4
Beyond artificial intelligence: exploring artificial wisdom.
In: International psychogeriatrics, vol 32, iss 8 (2020)
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5
HIV and three dimensions of Wisdom: Association with cognitive function and physical and mental well-being: For: Psychiatry Research.
Vásquez, Elizabeth; Lee, Ellen E; Zhang, Weihui. - : eScholarship, University of California, 2020
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6
HIV and Three Dimensions of Wisdom: Association with Cognitive function and Physical and Mental Well-Being
In: Psychiatry Res (2020)
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7
Beyond Artificial Intelligence (AI): Exploring Artificial Wisdom (AW)
In: Int Psychogeriatr (2020)
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8
Artificial Intelligence for Mental Health and Mental Illnesses: an Overview.
In: Current psychiatry reports, vol 21, iss 11 (2019)
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9
Artificial Intelligence Approaches to Predicting and Detecting Cognitive Decline in Older Adults: A Conceptual Review
In: Psychiatry Res (2019)
Abstract: Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: 1) sociodemographics; 2) clinical and psychometric assessments; 3) neuroimaging and neurophysiology; 4) electronic health records and claims; 5) novel assessments (e.g., sensors for digital data); and 6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders.
Keyword: Article
URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7081667/
https://doi.org/10.1016/j.psychres.2019.112732
http://www.ncbi.nlm.nih.gov/pubmed/31978628
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