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Mendeley readers
Attention Score in Context
| Title |
Secure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE)
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, July 2016
|
| DOI | 10.1186/s12911-016-0316-1 |
| Pubmed ID | |
| Authors |
Haoyi Shi, Chao Jiang, Wenrui Dai, Xiaoqian Jiang, Yuzhe Tang, Lucila Ohno-Machado, Shuang Wang |
| Abstract |
In biomedical research, data sharing and information exchange are very important for improving quality of care, accelerating discovery, and promoting the meaningful secondary use of clinical data. A big concern in biomedical data sharing is the protection of patient privacy because inappropriate information leakage can put patient privacy at risk. In this study, we deployed a grid logistic regression framework based on Secure Multi-party Computation (SMAC-GLORE). Unlike our previous work in GLORE, SMAC-GLORE protects not only patient-level data, but also all the intermediary information exchanged during the model-learning phase. The experimental results demonstrate the feasibility of secure distributed logistic regression across multiple institutions without sharing patient-level data. In this study, we developed a circuit-based SMAC-GLORE framework. The proposed framework provides a practical solution for secure distributed logistic regression model learning. |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 3% |
| Unknown | 31 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 28% |
| Researcher | 4 | 13% |
| Student > Master | 3 | 9% |
| Other | 2 | 6% |
| Professor > Associate Professor | 2 | 6% |
| Other | 5 | 16% |
| Unknown | 7 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 12 | 38% |
| Biochemistry, Genetics and Molecular Biology | 3 | 9% |
| Medicine and Dentistry | 3 | 9% |
| Physics and Astronomy | 1 | 3% |
| Unspecified | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 10 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 January 2022.
All research outputs
#8,117,281
of 24,353,295 outputs
Outputs from BMC Medical Informatics and Decision Making
#806
of 2,075 outputs
Outputs of similar age
#133,229
of 372,821 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#20
of 39 outputs
Altmetric has tracked 24,353,295 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,075 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has gotten more attention than average, scoring higher than 57% of its peers.
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 372,821 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.