Evidence map›Paper›PMID 35723468›Full record

ArticleAging2022

Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability.

Fedor Galkin, Kirill Kochetov, Michelle Keller, Alex Zhavoronkov, Nancy Etcoff

Abstract read
In one paragraph

Article in Aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. The topology of adolescent mental health.medRxiv : the preprint server for health sciences · 2026
    Article
  2. Multimodal Transformers and Their Applications in Drug Target Discovery for Aging and Age-Related Diseases.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024
    Review
  3. Review
  4. Article
  5. Article
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

5 authors.

Fedor GalkinDeep Longevity Limited, Hong Kong.
Kirill KochetovDeep Longevity Limited, Hong Kong.
Michelle KellerDeep Longevity Limited, Hong Kong.
Alex ZhavoronkovDeep Longevity Limited, Hong Kong.
Nancy EtcoffDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA.

Funding

U19 MIDlife in the United States-Diversity Supplement-WellsU19AG051426 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI CAROL D. RYFF · 2016 to 2026
$74.8M
STATISTICS COREP01AG020166 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI RYFF, CAROL D. · 2002 to 2015
$50.6M
NIA NIH HHS P01 AG020166NIA NIH HHS U19 AG051426
6 · The paper itself

Abstract

In this article, we present a deep learning model of human psychology that can predict one's current age and future well-being. We used the model to demonstrate that one's baseline well-being is not the determining factor of future well-being, as posited by hedonic treadmill theory. Further, we have created a 2D map of human psychotypes and identified the regions that are most vulnerable to depression. This map may be used to provide personalized recommendations for maximizing one's future well-being.

Indexed as

Artificial IntelligenceNeural Networks, ComputerAlgorithmsHumansagingartificial intelligencedepressionself-organizing mapswell-being

Identifiers

PMID35723468
PMCPMC9271294

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.