Evidence map›Paper›PMID 35124607›Full record

ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2022

Research on public health crisis early warning system based on context awareness.

Yingzhi Li, Laiyuan Xiao

Abstract read
In one paragraph

Article in Technology and health care : official journal of the European Society for Engineering 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, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. 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

2 authors.

Yingzhi LiDivision of Information Science and Technology, Wenhua University, Wuhan, Hubei, China.
Laiyuan XiaoSchool of Software, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the continuous expansion of urban scale and the increasing concentration of population, public health crisis has become an important part of urban residents' health management. The outbreak of the COVID-19 pandemic in Wuhan in 2020 has sounded the alarm.

objectiveWith the government at all levels to carry out the construction of urban Internet of things and information internet, the Internet backbone network has been built, deployed a large number of sensors, and collected a large number of urban situation data.

methodsIn this paper, situational awareness technology is introduced into public health emergency services.

resultsBy constructing ontology, situational data and residents' health data are integrated. Through key technologies such as situational data collection, data fusion and data mining, real-time perception of environmental conditions of public health emergency scene is realized, and situational data fusion and situational information reasoning model are constructed.

conclusionsThe model is applied to the public health crisis emergency simulation system to verify the effectiveness of the model.

Indexed as

COVID-19PandemicsDisease OutbreaksHumansPublic HealthSARS-CoV-2ontology technologypublic health emergencySituational awareness

Identifiers

PMID35124607
PMCPMC9028670

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.