Evidence map›Paper›PMID 35872411›Full record

ArticleComputers in biology and medicine2022

Construction and application of COVID-19 infectors activity information knowledge graph.

Liming Chen, Dong Liu, Junkai Yang, Mingyue Jiang, Shouqiang Liu, Yang Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Liming ChenSchool of Electronics and Information Engineering, Faculty of Engineering, South China Normal University, Nanhai, China. Electronic address: chenlimingpaul@qq.com.
Dong LiuSouth China Business College Guangdong University of Foreign Studies, Network and Information Technology Center, Guangzhou, China. Electronic address: liudong@gwng.edu.cn.
Junkai YangInternational Business College, South China Normal University, Nanhai, China. Electronic address: jkyang@ibc.scnu.edu.cn.
Mingyue JiangSchool of Electronics and Information Engineering, Faculty of Engineering, South China Normal University, Nanhai, China. Electronic address: stellamingyuejiang@qq.com.
Shouqiang LiuSchool of Artificial Intelligence, Faculty of Engineering, South China Normal University, Nanhai, China. Electronic address: liusq@m.scnu.edu.cn.
Yang WangSchool of Physics and Telecommunications Engineering, South China Normal University, Guangzhou, China. Electronic address: yangwang@m.scnu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During COVID-19 prevention and control, people need to be aware of the outbreak situation in their area to avoid being inconvenienced by the outbreak and even becoming infected. Thus, this project constructs a knowledge graph with COVID-19 infector activity information, by using the official flow information of the infected people from the provincial and municipal websites. This knowledge graph is the basis of the COVID-19 applications for tracing, visualization and reporting proposes. In the implementation process, we (1) collect a dataset with the information on COVID-19 cases from the prevention and control centers, (2) extract the entity elements with a Bert + BILSTM + CRF-based model, and (3) pre-process the dataset and construct a knowledge graph with manual annotation and human-based review. Finally, we use the knowledge graph to develop a web-based application to implement the question and answer, query, transmission path tracking and the "No.0" tracing infector functions.

Indexed as

COVID-19HumansPattern Recognition, AutomatedSoftwareApplication of knowledge graphCOVID-19Knowledge graphKnowledge reasoningNER

Identifiers

PMID35872411
PMCPMC9293382

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.