Evidence map›Paper›PMID 35469206›Full record

ArticleComputational intelligence and neuroscience2022

A Multitask Deep Learning Framework for DNER.

Ran Jin, Tengda Hou, Tongrui Yu, Min Luo, Haoliang Hu

Abstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2022. 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

5 authors.

Ran JinCollege of Big Data and Software Engineering, Zhejiang Wanli University, No. 8 South Qianhu Road, Ningbo, China.ORCID https://orcid.org/0000-0002-1405-1189
Tengda HouCollege of Big Data and Software Engineering, Zhejiang Wanli University, No. 8 South Qianhu Road, Ningbo, China.ORCID https://orcid.org/0000-0002-4900-3718
Tongrui YuCollege of Big Data and Software Engineering, Zhejiang Wanli University, No. 8 South Qianhu Road, Ningbo, China.ORCID https://orcid.org/0000-0002-6348-4677
Min LuoNingbo University of Finance & Economics, No. 899 College Road, Ningbo, China.ORCID https://orcid.org/0000-0002-3178-6259
Haoliang HuCollege of Big Data and Software Engineering, Zhejiang Wanli University, No. 8 South Qianhu Road, Ningbo, China.ORCID https://orcid.org/0000-0002-3629-0682

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the years, the explosive growth of drug-related text information has resulted in heavy loads of work for manual data processing. However, the domain knowledge hidden is believed to be crucial to biomedical research and applications. In this article, the multi-DTR model that can accurately recognize drug-specific name by joint modeling of DNER and DNEN was proposed. Character features were extracted by CNN out of the input text, and the context-sensitive word vectors were obtained using ELMo. Next, the pretrained biomedical words were embedded into BiLSTM-CRF and the output labels were interacted to update the task parameters until DNER and DNEN would support each other. The proposed method was found with better performance on the DDI2011 and DDI2013 datasets.

Indexed as

Deep LearningNeural Networks, ComputerResearch Design

Identifiers

PMID35469206
PMCPMC9034928

What OpenQuestion holds

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LicenceCC BY
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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.