Evidence map›Paper›PMID 34302549›Full record

ReviewJournal of medical systems2021

Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China.

Jiancheng Dong, Huiqun Wu, Dong Zhou, Kaixiang Li, Yuanpeng Zhang, Hanzhen Ji, Zhuang Tong, Shuai Lou, Zhangsuo Liu

Open access · hybridAbstract readReview
In one paragraph

Review in Journal of medical systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 3 pooled it
7.1field-weighted citation impact, top 2% of its field
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

29 citing papers in PubMed, 3 syntheses or guidelines pooled it, 72 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Use of Digital Tools in Arbovirus Surveillance: Scoping Review.Journal of medical Internet research · 2024
    Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Harnessing AI for public health: India's roadmap.Frontiers in public health · 2024
    Article
  18. Article
  19. Article
  20. 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 at 4 institutions in 2 countries.

Jiancheng DongMedical Big Data Research Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. dongjc@ntu.edu.cn.ORCID http://orcid.org/0000-0002-9646-2853
Huiqun WuDepartment of Medical Informatics, Medical School of Nantong University, Nantong, China.
Dong ZhouDepartment of Medical Informatics, Medical School of Nantong University, Nantong, China.
Kaixiang LiMedical Big Data Research Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yuanpeng ZhangDepartment of Medical Informatics, Medical School of Nantong University, Nantong, China.
Hanzhen JiThe Third Affiliated Hospital of Nantong University, Nantong, China.
Zhuang TongMedical Big Data Research Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Shuai LouJiangsu Zhongkang Software Co, Ltd, Nantong, China.
Zhangsuo LiuMedical Big Data Research Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. zhangsuoliu@zzu.edu.cn.
First Affiliated Hospital of Zhengzhou University · CNNantong University · CNHong Kong Polytechnic University · HKWuxi Third People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.

Indexed as

COVID-19PandemicsArtificial IntelligenceBig DataChinaHumansSARS-CoV-2Artificial intelligenceBig dataCOVID-19Deep learningEpidemic prevention and control

Identifiers

PMID34302549
PMCPMC8308073
OpenAlexW3186170836

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.