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Expressive language development in adolescents with Down syndrome and fragile X syndrome: change over time and the role of family-related factors.
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In: Journal of neurodevelopmental disorders, vol 12, iss 1 (2020)
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Depressive Symptom Profiles Predict Specific Neurodegenerative Disease Syndromes in Early Stages.
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Utility of the global CDR® plus NACC FTLD rating and development of scoring rules: Data from the ARTFL/LEFFTDS Consortium.
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In: Alzheimer's & dementia : the journal of the Alzheimer's Association, vol 16, iss 1 (2020)
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Use of the CDR® plus NACC FTLD in mild FTLD: Data from the ARTFL/LEFFTDS consortium.
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In: Alzheimer's & dementia : the journal of the Alzheimer's Association, vol 16, iss 1 (2020)
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Depressive Symptom Profiles Predict Specific Neurodegenerative Disease Syndromes in Early Stages.
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Utility of the global CDR® plus NACC FTLD rating and development of scoring rules: Data from the ARTFL/LEFFTDS Consortium.
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In: Alzheimer's & dementia : the journal of the Alzheimer's Association, vol 16, iss 1 (2020)
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| 7 |
Use of the CDR® plus NACC FTLD in mild FTLD: Data from the ARTFL/LEFFTDS consortium.
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In: Alzheimer's & dementia : the journal of the Alzheimer's Association, vol 16, iss 1 (2020)
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| 8 |
A neurophysiological model of speech production deficits in fragile X syndrome.
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In: Brain communications, vol 2, iss 1 (2020)
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| 9 |
Screen time in 36-month-olds at increased likelihood for ASD and ADHD.
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Screen time in 36-month-olds at increased likelihood for ASD and ADHD.
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Molecular Biomarkers Predictive of Sertraline Treatment Response in Young Children With Autism Spectrum Disorder.
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A Therapeutic Relational Agent for Reducing Problematic Substance Use (Woebot): Development and Usability Study (Preprint)
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| 13 |
The Relationship between Expressive Language Sampling and Clinical Measures in Fragile X Syndrome and Typical Development.
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In: Brain sciences, vol 10, iss 2 (2020)
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| 14 |
Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.
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Graham, Sarah A; Lee, Ellen E; Jeste, Dilip V; Van Patten, Ryan; Twamley, Elizabeth W; Nebeker, Camille; Yamada, Yasunori; Kim, Ho-Cheol; Depp, Colin A. - : eScholarship, University of California, 2020
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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.
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Keyword:
4.1 Discovery and preclinical testing of markers and technologies; 80 and over; Aged; Aging; Algorithms; Artificial Intelligence; Basic Behavioral and Social Science; Behavioral and Social Science; Brain Disorders; Clinical Research; Cognitive Dysfunction; Data Interpretation; Dementia; Electronic Health Records; Genomics; Humans; Machine Learning; Medical and Health Sciences; Mental Health; Mild cognitive impairment; Natural Language Processing; Neurosciences; Psychiatry; Psychology and Cognitive Sciences; Sensors; Statistical
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URL: https://escholarship.org/uc/item/21q1z3qm
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| 15 |
Contributions of common genetic variants to risk of schizophrenia among individuals of African and Latino ancestry.
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In: Molecular psychiatry, vol 25, iss 10 (2020)
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Contributions of common genetic variants to risk of schizophrenia among individuals of African and Latino ancestry.
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In: Molecular psychiatry, vol 25, iss 10 (2020)
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Health comorbidities and cognitive abilities across the lifespan in Down syndrome. ...
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Клинико-лингвистические характеристики психических нарушений при ВИЧ-инфицировании ... : Clinical and linguistic characteristics of mental disorders in HIV infection ...
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Assessing Health Related Quality of Life, Language Impairment, and Psychosocial Factors in Post-Stroke Aphasia
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In: Honors College (2020)
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Специфика обследования и коррекции речевых нарушений у пациентки с подкорковой афазией ... : Examination and rehabilitation of speech disorders in a patient with subcortical aphasia ...
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