Evidence map›Paper›PMID 32938366›Full record

ArticleBMC bioinformatics2020

Biomedical document triage using a hierarchical attention-based capsule network.

Jian Wang, Mengying Li, Qishuai Diao, Hongfei Lin, Zhihao Yang, YiJia Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2020. 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

6 authors.

Jian WangDalian University of Technology, The School of Computer Science and Technology, Dalian, 116024, China.
Mengying LiDalian University of Technology, The School of Computer Science and Technology, Dalian, 116024, China.
Qishuai DiaoDalian University of Technology, The School of Computer Science and Technology, Dalian, 116024, China.
Hongfei LinDalian University of Technology, The School of Computer Science and Technology, Dalian, 116024, China.
Zhihao YangDalian University of Technology, The School of Computer Science and Technology, Dalian, 116024, China.
YiJia ZhangDalian University of Technology, The School of Computer Science and Technology, Dalian, 116024, China. zhyj@dlut.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBiomedical document triage is the foundation of biomedical information extraction, which is important to precision medicine. Recently, some neural networks-based methods have been proposed to classify biomedical documents automatically. In the biomedical domain, documents are often very long and often contain very complicated sentences. However, the current methods still find it difficult to capture important features across sentences.

resultsIn this paper, we propose a hierarchical attention-based capsule model for biomedical document triage. The proposed model effectively employs hierarchical attention mechanism and capsule networks to capture valuable features across sentences and construct a final latent feature representation for a document. We evaluated our model on three public corpora.

conclusionsExperimental results showed that both hierarchical attention mechanism and capsule networks are helpful in biomedical document triage task. Our method proved itself highly competitive or superior compared with other state-of-the-art methods.

Indexed as

Neural Networks, ComputerHumansInformation Storage and RetrievalTriageBiomedical document triageBiomedical literatureCapsule networkHierarchical attention mechanism

Identifiers

PMID32938366
PMCPMC7495737

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.