Evidence map›Paper›PMID 38142162›Full record

ReviewThe American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry2024

Deep Learning and Geriatric Mental Health.

Howard Aizenstein, Raeanne C Moore, Ipsit Vahia, Adam Ciarleglio

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Psychotherapy for older adults with depression: a narrative review.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
    Review
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

4 authors.

Howard AizensteinDepartment of Psychiatry (HA), University of Pittsburgh School of Medicine, Pittsburgh, PA. Electronic address: aizen@pitt.edu.
Raeanne C MooreDepartment of Psychiatry (RCM), University of California San Diego, San Diego, CA.
Ipsit VahiaDivision of Geriatric Psychiatry (IV), Harvard Medical School, Boston, MA.
Adam CiarleglioDepartment of Biostatistics and Bioinformatics (AC), George Washington University, Washington, D.C.

Funding

Validating novel sleep sensors and devices in older adults with Alzheimer's diseaseP30AG073107 · NIA · UNIVERSITY OF MASSACHUSETTS AMHERST · PI Sudeshna Das · 2021 to 2026
$32.0M
Roles of Gray Matter Brain Aging and Small Vessel Disease in AD PathophysiologyRF1AG025516 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI AIZENSTEIN, HOWARD J, VILLEMAGNE, VICTOR LUIS · 2014 to 2021
$9.0M
Pharmacologic MRI Predictors of Treatment Response in Late-Life DepressionR01MH076079 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI AIZENSTEIN, HOWARD J, ANDREESCU, CARMEN · 2006 to 2022
$7.1M
Effects of Orally Administered Nicotinamide Riboside on Bioenergetic Metabolism, Oxidative Stress and Cognition in Mild Cognitive Impairment and Mild Alzheimer's DementiaR01AG066670 · NIA · MCLEAN HOSPITAL · PI DU, FEI, FORESTER, BRENT PETER · 2020 to 2024
$4.0M
Identifying Digital Phenotypes of Risk for Alzheimer's Disease and Related Dementias Among Hispanics/LatinosR01AG070956 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MARQUINE, MARIA, MOORE, RAEANNE CRISTINE · 2021 to 2025
$3.9M
Statistical and Machine Learning Methods for Integrating Clinical and Multimodal Imaging Data to Select Optimal Antidepressant TreatmentK01MH113850 · NIMH · GEORGE WASHINGTON UNIVERSITY · PI CIARLEGLIO, ADAM · 2018 to 2021
$647k
NIA NIH HHS P30 AG073107NIA NIH HHS R01 AG066670NIA NIH HHS R01 AG070956NIA NIH HHS RF1 AG025516NIMH NIH HHS K01 MH113850NIMH NIH HHS R01 MH076079
6 · The paper itself

Abstract

The goal of this overview is to help clinicians develop basic proficiency with the terminology of deep learning and understand its fundamentals and early applications. We describe what machine learning and deep learning represent and explain the underlying data science principles. We also review current promising applications and identify ethical issues that bear consideration. Deep Learning is a new type of machine learning that is remarkably good at finding patterns in data, and in some cases generating realistic new data. We provide insights into how deep learning works and discuss its relevance to geriatric psychiatry.

Indexed as

Deep LearningMental HealthAgedGeriatric PsychiatryHumansMachine LearningDeep learninggeriatric mental healthmachine learningneuroimaging

Identifiers

PMID38142162
PMCPMC10922602

What OpenQuestion holds

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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.