Evidence map›Paper›PMID 35469219›Full record

ArticleComputational and mathematical methods in medicine2022

Deep Learning-Based Medical Information System in First Aid of Surgical Trauma.

Yong Liang, Yugeng Liu, Bo Liu, Aimin Xu, Junyu Wang

Open access · hybridAbstract read
In one paragraph

Article in Computational and mathematical methods in medicine, 2022. 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
1.0field-weighted citation impact, top 25% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. [Application and research progress of artificial intelligence technology in trauma treatment].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2023
    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

5 authors at 1 institution in 1 country.

Yong LiangDepartment of Emergency, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100043, China.ORCID https://orcid.org/0000-0002-6782-9935
Yugeng LiuDepartment of Emergency, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100043, China.ORCID https://orcid.org/0000-0001-6198-4873
Bo LiuDepartment of Emergency, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100043, China.ORCID https://orcid.org/0000-0003-2979-6113
Aimin XuDepartment of Emergency, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100043, China.ORCID https://orcid.org/0000-0001-5585-0182
Junyu WangEmergency Medicine Clinical Research Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing 100043, China.ORCID https://orcid.org/0000-0003-0508-2327
Capital Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study was to explore the application of process reengineering integration in trauma first aid based on deep learning and medical information system. According to the principles and methods of process reengineering, based on the analysis of the problems and causes of the original trauma first aid process, a new set of trauma first aid integration process is established. The Deep Belief Network (DBN) in deep learning is used to optimize the travel path of emergency vehicles, and the accuracy of travel path prediction of emergency vehicles under different environmental conditions is analyzed. DBN is applied to the surgical clinic of the hospital to verify the applicability of this method. The results showed that in the analysis of sample abscission, the abscission rates of the two groups were 2.23% and 0.78%, respectively. In the analysis of the trauma severity (TI) score between the two groups, more than 60% of the patients were slightly injured, and there was no significant difference (

Indexed as

Deep LearningFirst AidHumansInformation SystemsResearch Design

Identifiers

PMID35469219
PMCPMC9034939
OpenAlexW4224120932

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.