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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Advances in intelligent diagnosis methods for pulmonary ground-glass opacity nodules
|
|---|---|
| Published in |
BioMedical Engineering OnLine, February 2018
|
| DOI | 10.1186/s12938-018-0435-2 |
| Pubmed ID | |
| Authors |
Jing Yang, Hailin Wang, Chen Geng, Yakang Dai, Jiansong Ji |
| Abstract |
Pulmonary nodule is one of the important lesions of lung cancer, mainly divided into two categories of solid nodules and ground glass nodules. The improvement of diagnosis of lung cancer has significant clinical significance, which could be realized by machine learning techniques. At present, there have been a lot of researches focusing on solid nodules. But the research on ground glass nodules started late, and lacked research results. This paper summarizes the research progress of the method of intelligent diagnosis for pulmonary nodules since 2014. It is described in details from four aspects: nodular signs, data analysis methods, prediction models and system evaluation. This paper aims to provide the research material for researchers of the clinical diagnosis and intelligent analysis of lung cancer, and further improve the precision of pulmonary ground glass nodule diagnosis. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 41 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 20% |
| Researcher | 5 | 12% |
| Student > Ph. D. Student | 4 | 10% |
| Student > Postgraduate | 3 | 7% |
| Lecturer > Senior Lecturer | 2 | 5% |
| Other | 4 | 10% |
| Unknown | 15 | 37% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 9 | 22% |
| Engineering | 5 | 12% |
| Computer Science | 5 | 12% |
| Business, Management and Accounting | 1 | 2% |
| Biochemistry, Genetics and Molecular Biology | 1 | 2% |
| Other | 0 | 0% |
| Unknown | 20 | 49% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 September 2018.
All research outputs
#20,532,290
of 23,102,082 outputs
Outputs from BioMedical Engineering OnLine
#692
of 825 outputs
Outputs of similar age
#376,245
of 438,198 outputs
Outputs of similar age from BioMedical Engineering OnLine
#18
of 19 outputs
Altmetric has tracked 23,102,082 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 825 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 438,198 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.