Evidence map›Paper›PMID 31014444›Full record

ReviewZhongguo fei ai za zhi = Chinese journal of lung cancer2019

[An Overview of the Application of Artificial Neural Networks in Lung Cancer Research].

Xingyu Zhu, Nan Chen, Lunxu Liu, Qiang Pu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Zhongguo fei ai za zhi = Chinese journal of lung cancer, 2019. 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

4 authors.

Xingyu ZhuWest China School of Medicine, Sichuan University, Chengdu 610041, China.
Nan ChenWest China School of Medicine, Sichuan University, Chengdu 610041, China.
Lunxu LiuDepartment of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China.
Qiang PuDepartment of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer is the most common and fatal tumor in the world with limited diagnostic and treatment methods. The development of precision medicine has brought new opportunities for the improvement of diagnosis and treatment of lung cancer. However, various kinds of information required by precision medicine (such as biometrics, clinical test indicators and non-biological environmental background information) are difficult for clinicians to integrate and utilize effectively. With the development of computer technology, artificial neural networks (ANNs), which has the characteristic of high fault tolerance, intelligence and self-learning ability. Its powerful information integration ability can solve many problems in the development and application of precision medicine, which can play a key role in basic research and clinical practice associated with lung cancer. This article reviewed the application of artificial neural network in the field of lung cancer.
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Indexed as

Lung NeoplasmsNeural Networks, ComputerBiomedical ResearchHumansArtificial neural networksLung neoplasmsPrecision medicine

Identifiers

PMID31014444
PMCPMC6500498

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.