Evidence map›Paper›PMID 31978628›Full record

ReviewPsychiatry research2020

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.

Sarah A Graham, Ellen E Lee, Dilip V Jeste, Ryan Van Patten, Elizabeth W Twamley, Camille Nebeker, Yasunori Yamada, Ho-Cheol Kim, Colin A Depp

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Psychiatry research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
78citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

78 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  13. Bridging Gaps in Sundown Syndrome Research: a Scoping Review and Roadmap for Future Multimodal Approaches.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2025
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18 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Sarah A GrahamDepartment of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States.
Ellen E LeeDepartment of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; VA San Diego Healthcare System, San Diego, CA, United States.
Dilip V JesteDepartment of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; Department of Neurosciences, University of California San Diego, La Jolla, CA, United States. Electronic address: djeste@health.ucsd.edu.
Ryan Van PattenDepartment of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States.
Elizabeth W TwamleyDepartment of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; VA San Diego Healthcare System, San Diego, CA, United States.
Camille NebekerSam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, United States.
Yasunori YamadaAccessibility and Aging, IBM Research-Tokyo, Tokyo, Japan.
Ho-Cheol KimIBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; Scalable Knowledge Intelligence, IBM Research-Almaden, San Jose, CA, United States.
Colin A DeppDepartment of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; VA San Diego Healthcare System, San Diego, CA, United States.

Funding

UC San Diego Clinical and Translational Research InstituteUL1TR001442 · NCATS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FIRESTEIN, GARY S, HOGARTH, MICHAEL · 2015 to 2024
$88.3M
FELLOWSHIP IN GERIATRIC MENTAL HEALTHT32MH019934 · NIMH · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Elizabeth W Twamley · 1994 to 2026
$9.5M
Inflammatory Mechanisms Linking Sleep Disturbances and Cognitive Deficits in Schizophrenia in Later LifeK23MH119375 · NIMH · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LEE, ELLEN EUN-OK · 2019 to 2023
$1.0M
NCATS NIH HHS UL1 TR001442NIMH NIH HHS K23 MH119375NIMH NIH HHS T32 MH019934
6 · The paper itself

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.

Indexed as

AlgorithmsAgedAged, 80 and overArtificial IntelligenceCognitive DysfunctionData Interpretation, StatisticalElectronic Health RecordsGenomicsHumansMachine LearningNatural Language ProcessingDementiaMachine learningMild cognitive impairmentNatural language processingSensors

Identifiers

PMID31978628
PMCPMC7081667

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.