Evidence map›Paper›PMID 40041765›Full record

ArticleProceedings of the ... International World-Wide Web Conference. International WWW Conference2024

Uncertainty-Aware Pre-Trained Foundation Models for Patient Risk Prediction via Gaussian Process.

Jiaying Lu, Shifan Zhao, Wenjing Ma, Hui Shao, Xiao Hu, Yuanzhe Xi, Carl Yang

Abstract read
In one paragraph

Article in Proceedings of the ... International World-Wide Web Conference. International WWW Conference, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Jiaying LuDepartment of Computer Science & Nell Hodgson Woodruff School of Nursing, Emory University.
Shifan ZhaoDepartment of Mathematics, Emory University.
Wenjing MaDepartment of Biostatistics, University of Michigan.
Hui ShaoHubert Department of Global Health & Department of Family and Preventive Medicine, Emory University.
Xiao HuNell Hodgson Woodruff School of Nursing, Emory University; The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University.
Yuanzhe XiDepartment of Mathematics, Emory University.
Carl YangDepartment of Computer Science, Emory University.

Funding

Understanding Diabetes Heterogeneity via Mining Multimodality Interconnected DataK25DK135913 · NIDDK · EMORY UNIVERSITY · PI Ji Carl Yang · 2023 to 2026
$687k
NIDDK NIH HHS K25 DK135913
6 · The paper itself

Abstract

Patient risk prediction models are crucial as they enable healthcare providers to proactively identify and address potential health risks. Large pre-trained foundation models offer remarkable performance in risk prediction tasks by analyzing multimodal patient data. However, a notable limitation of pre-trained foundation models lies in their deterministic predictions (

Indexed as

Clinical Foundation ModelsGaussian Process ClassificationPatient Risk PredictionUncertainty Quantification

Identifiers

PMID40041765
PMCPMC11876793

What OpenQuestion holds

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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.