Evidence map›Paper›PMID 35494891›Full record

ArticleHealth information science and systems2022

CD-Surv: a contrastive-based model for dynamic survival analysis.

Caogen Hong, Jinbiao Chen, Fan Yi, Yuzhe Hao, Fanwen Meng, Zhanghuiya Dong, Hui Lin, Zhengxing Huang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Health information science and 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. 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.

Caogen HongZhejiang University, Hangzhou, Zhejiang China.
Jinbiao ChenZhejiang University, Hangzhou, Zhejiang China.
Fan YiZhejiang University, Hangzhou, Zhejiang China.
Yuzhe HaoJiangsu Automation Research Institute, Lianyungang, China.
Fanwen MengJiangsu Automation Research Institute, Lianyungang, China.
Zhanghuiya DongJiangsu Automation Research Institute, Lianyungang, China.
Hui LinZhejiang University, Hangzhou, Zhejiang China.
Zhengxing HuangZhejiang University, Hangzhou, Zhejiang China.ORCID 0000-0002-2644-8642

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Survival analysis, aimed at investigating the relationships between covariates and event time, has exhibited profound effects on health service management. Longitudinal data with sequential patterns, such as electronic health records (EHRs), contain a large volume of patient treatment trajectories, and therefore, provide great potential for survival analysis. However, most existing studies address the survival analysis problem in a static manner, that is, they only utilize a fraction of longitudinal data, ignore the correlations between multiple visits, and usually may not be able to capture the latent representations of patient treatment trajectories. This inevitably deteriorates the performance of the survival analysis. To address this challenge, we propose an end-to-end contrastive-based model

Indexed as

Contrastive learningElectronic health recordsLongitudinal dataSurvival analysis

Identifiers

PMID35494891
PMCPMC9005562

What OpenQuestion holds

Textmetadata
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.