Evidence map›Paper›PMID 36816520›Full record

ArticleInternational journal of intelligent systems2022

A hybrid adaptive approach for instance transfer learning with dynamic and imbalanced data.

Xiangzhou Zhang, Kang Liu, Borong Yuan, Hongnian Wang, Shaoyong Chen, Yunfei Xue, Weiqi Chen, Mei Liu, Yong Hu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of intelligent systems, 2022. 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. Review
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

9 authors.

Xiangzhou ZhangBig Data Decision Institute, Jinan University, Guangzhou, China.
Kang LiuBig Data Decision Institute, Jinan University, Guangzhou, China.
Borong YuanBig Data Decision Institute, Jinan University, Guangzhou, China.
Hongnian WangBig Data Decision Institute, Jinan University, Guangzhou, China.
Shaoyong ChenBig Data Decision Institute, Jinan University, Guangzhou, China.
Yunfei XueBig Data Decision Institute, Jinan University, Guangzhou, China.
Weiqi ChenBig Data Decision Institute, Jinan University, Guangzhou, China.
Mei LiuDivision of Medical Informatics, University of Kansas Medical Center, Kansas City, KS, United States of America.
Yong HuBig Data Decision Institute, Jinan University, Guangzhou, China.

Funding

RADx-UP: Improving the Response of Local Urban and Rural Communities to Disparities in Covid-19 TestingUL1TR002366 · NCATS · UNIVERSITY OF KANSAS MEDICAL CENTER · PI Mario Castro, JAMES STEVEN LEEDER · 2017 to 2026
$44.3M
Identifying Personalized Risk of Acute Kidney Injury with Machine LearningR01DK116986 · NIDDK · UNIVERSITY OF KANSAS MEDICAL CENTER · PI LIU, MEI · 2019 to 2021
$1.5M
NCATS NIH HHS UL1 TR002366NIDDK NIH HHS R01 DK116986
6 · The paper itself

Abstract

Machine learning has demonstrated success in clinical risk prediction modeling with complex electronic health record data. However, the evolving nature of clinical practices can dynamically change the underlying data distribution over time, leading to model performance drift. Adopting an outdated model is potentially risky and may result in unintentional losses. In this paper, we propose a novel

Indexed as

Clinical risk predictionElectronic health recordsImbalanced dataPerformance driftTransfer learning

Identifiers

PMID36816520
PMCPMC9936919

What OpenQuestion holds

Textmetadata
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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.