Evidence map›Paper›PMID 38712223›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Predicting Risk of Alzheimer's Diseases and Related Dementias with AI Foundation Model on Electronic Health Records.

Weicheng Zhu, Huanze Tang, Hao Zhang, Haresh Rengaraj Rajamohan, Shih-Lun Huang, Xinyue Ma, Ankush Chaudhari, Divyam Madaan, Elaf Almahmoud, Sumit Chopra and 4 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 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. 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

14 authors.

Weicheng ZhuNYU, Center for Data Science, New York, NY, 10001, USA.ORCID 0000-0002-6037-7825
Huanze TangNYU, Center for Data Science, New York, NY, 10001, USA.
Hao ZhangNYU Grossman School of Medicine, Department of Population Health, New York, NY, 10016, USA.
Haresh Rengaraj RajamohanNYU, Center for Data Science, New York, NY, 10001, USA.
Shih-Lun HuangNYU, Center for Data Science, New York, NY, 10001, USA.
Xinyue MaNYU, Center for Data Science, New York, NY, 10001, USA.
Ankush ChaudhariNYU, Center for Data Science, New York, NY, 10001, USA.
Divyam MadaanNYU, Courant Institute of Mathematical Sciences, New York, NY, 10001, USA.
Elaf AlmahmoudNYU, Courant Institute of Mathematical Sciences, New York, NY, 10001, USA.
Sumit ChopraNYU, Courant Institute of Mathematical Sciences, New York, NY, 10001, USA.
John A DodsonNYU Grossman School of Medicine, Department of Population Health, New York, NY, 10016, USA.
Abraham A BrodyNYU Grossman School of Medicine, Department of Medicine, New York, NY, 10016, USA.
Arjun V MasurkarNYU Grossman School of Medicine, Department of Neurology, New York, NY, 10016, USA.
Narges RazavianNYU Grossman School of Medicine, Department of Population Health, New York, NY, 10016, USA.

Funding

Research Education ComponentP30AG066512 · NIA · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Mary Sherman Mittelman · 2020 to 2026
$28.4M
Leveraging Electronic Health Records for Reducing Dementia Screening Disparities in Diverse CommunitiesR01AG079175 · NIA · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Abraham Aizer Brody, John A Dodson · 2023 to 2026
$3.3M
SCH: Generalizable Dementia Early Detection via Multimodal Self-Supervised LearningR01AG085617 · NIA · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Arjun Vijay Masurkar, Narges Razavian · 2023 to 2026
$1.1M
NIA NIH HHS P30 AG066512NIA NIH HHS R01 AG079175NIA NIH HHS R01 AG085617
6 · The paper itself

Abstract

Early identification of Alzheimer's disease (AD) and AD-related dementias (ADRD) has high clinical significance, both because of the potential to slow decline through initiating FDA-approved therapies and managing modifiable risk factors, and to help persons living with dementia and their families to plan before cognitive loss makes doing so challenging. However, substantial racial and ethnic disparities in early diagnosis currently lead to additional inequities in care, urging accurate and inclusive risk assessment programs. In this study, we trained an artificial intelligence foundation model to represent the electronic health records (EHR) data with a vast cohort of 1.2 million patients within a large health system. Building upon this foundation EHR model, we developed a predictive Transformer model, named

Identifiers

PMID38712223
PMCPMC11071573

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.