Evidence map›Paper›PMID 39949328›Full record

ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2023

A Practical Approach to Disease Risk Prediction: Focus on High-Risk Patients via Highest-

Hongyi Yang, Rich Gonzalez, Brahmajee K Nallamothu, Keith D Aaronson, Kevin R Ward, Alfred O Hero, Sardar Ansari

Abstract read
PubMed Full text at PMC Publisher
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. 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.

Hongyi YangDepartment of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.
Rich GonzalezDepartment of Psychology, University of Michigan, Ann Arbor, MI, USA.
Brahmajee K NallamothuDivision of Cardiology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Keith D AaronsonDivision of Cardiology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Kevin R WardDepartment of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA.
Alfred O HeroDepartment of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.
Sardar AnsariDepartment of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA.

Funding

Prediction of Heart Failure Onset using Multimodal Data Analysis, Deep Learning and Commercial WearablesK01HL155404 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ANSARI, SARDAR · 2021 to 2025
$817k
NHLBI NIH HHS K01 HL155404
6 · The paper itself

Abstract

Disease risk prediction models play an important role in preventing disease developments in modern healthcare. However, the lack of focus on high-risk patients has hindered the large-scale practical application of these models, especially considering the limitation of medical resources available for following up on patients who are deemed high-risk. In this study, we propose a novel and practical approach that focuses on minimizing the number of false positive observations among high-risk patients by introducing the

Indexed as

disease risk predictionHighest-k Losshighest k scoreshighest risksoft sorting

Identifiers

PMID39949328
PMCPMC11821551

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Registered trials

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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.