Evidence map›Paper›PMID 38404694›Full record

ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2023

Harnessing Transfer Learning for Dementia Prediction: Leveraging Sex-Different Mild Cognitive Impairment Prognosis.

Ziming Liu, Muskan Garg, Sunyang Fu, Surjodeep Sarkar, Maria Vassilaki, Ronald C Petersen, Jennifer St Sauver, Sunghwan Sohn

Abstract read
In one paragraph

Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Causal Explanation from Mild Cognitive Impairment Progression using Graph Neural Networks.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2024
    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

8 authors.

Ziming LiuMechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, USA.
Muskan GargDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.
Sunyang FuDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.
Surjodeep SarkarDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.
Maria VassilakiDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, USA.
Ronald C PetersenDepartment of Neurology, Mayo Clinic, Rochester, USA.
Jennifer St SauverDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, USA.
Sunghwan SohnDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.

Funding

SUPPLEMENT TO ALZHEIMERS DISEASE PATIENT REGISTRYU01AG006786 · NIA · MAYO CLINIC ROCHESTER · PI GRAFF-RADFORD, JONATHAN, JACK, CLIFFORD R. · 1986 to 2023
$49.6M
Research Education ComponentP30AG062677 · NIA · MAYO CLINIC ROCHESTER · PI Walter K. Kremers · 2019 to 2026
$33.5M
Disease pathways in the population determined by amyloid, tau, and neurodegeneration imaging biomarkersR37AG011378 · NIA · MAYO CLINIC ROCHESTER · PI CLIFFORD R. JACK · 2018 to 2026
$6.8M
Validating the New Criteria for Preclinical Alzheimer's diseaseR01AG041851 · NIA · MAYO CLINIC ROCHESTER · PI JACK, CLIFFORD R., KNOPMAN, DAVID S · 2012 to 2021
$6.3M
Early Detection of Mild Cognitive Impairment, Alzheimer’s Disease and Other Dementias using EHRR01AG068007 · NIA · MAYO CLINIC ROCHESTER · PI Yonas E Geda, Sunghwan Sohn · 2020 to 2026
$3.8M
Development, Validation, and Application of an Imaging based CVD ScaleR01NS097495 · NINDS · MAYO CLINIC ROCHESTER · PI VEMURI, PRASHANTHI · 2016 to 2020
$2.9M
NIA NIH HHS P30 AG062677NIA NIH HHS R01 AG041851NIA NIH HHS R01 AG068007NIA NIH HHS R37 AG011378NIA NIH HHS U01 AG006786NINDS NIH HHS R01 NS097495
6 · The paper itself

Abstract

This paper presents a machine learning-based prediction for dementia, leveraging transfer learning to reuse the knowledge learned from prediction of mild cognitive impairment, a precursor of dementia. We also examine the impacts of temporal aspects of longitudinal data and sex differences. The methodology encompasses key components such as setting the duration window, comparing different modeling strategies, conducting comprehensive evaluations, and examining the sex-specific impacts of simulated scenarios. The findings reveal that cognitive deficits in females, once detected at the mild cognitive impairment stage, tend to deteriorate over time, while males exhibit more diverse decline across various characteristics without highlighting specific ones. However, the underlying reasons for these sex differences remain unknown and warrant further investigation.

Indexed as

DementiaMild cognitive impairmentSex differencesTime series analysisTransfer Learning

Identifiers

PMID38404694
PMCPMC10883588

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.