Evidence map›Paper›PMID 36711055›Full record

ArticleComputers & electrical engineering : an international journal2023

Management for stroke intelligent early warning empowered by big data.

Xiaoyong Chen, Boxiong Yang, Shuai Zhao, Wei Wei, Jialu Chen, Jie Ding, Hong Wang, Peng Sun, Lin Gan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computers & electrical engineering : an international journal, 2023. 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

9 authors.

Xiaoyong ChenSchool of Health Industry Management, University of Sanya, Sanya, China.
Boxiong YangSchool of Information & Intelligence Engineering, University of Sanya, Sanya, China.
Shuai ZhaoSchool of Health Industry Management, University of Sanya, Sanya, China.
Wei WeiSchool of Health Industry Management, University of Sanya, Sanya, China.
Jialu ChenSanya Traditional Chinese Medicine Hospital, Sanya, China.
Jie DingWound Healing Research Group, Department of Surgery, Faculty of Medicine and Dentistry University of Alberta, Canada.
Hong WangSwiss TCM Clinic, Zurich, Switzerland.
Peng SunSanya People's Hospital, Sanya, China.
Lin GanSchool of Information & Intelligence Engineering, University of Sanya, Sanya, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Global aging population, especially with the global pandemic outbreak of the Corona Virus Disease 2019 (COVID-19), has endangered human health security. Digital information technology through big data empowerment and intelligent application is widely considered a key element to solve the problems. Stroke is a life-threaten disorder. We studied individual health management and disease risk perception using human health assessment model and make full use of wearable wireless sensor, Internet of Things, big data, and Artificial Intelligence for potential risk monitoring and real-time stroke warning. We proposed an effective method of monitoring, early warning and rescue to improve the stroke treatment. The result shows that the health management empowered by big data can generate new opportunities and ideas to solve early detection and warning of stroke.

Indexed as

Big dataDisease risk perceptionHealth managementIntelligent monitoringStroke warning

Identifiers

PMID36711055
PMCPMC9871473

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.