Evidence map›Paper›PMID 31141561›Full record

ArticlePloS one2019

PPCD: Privacy-preserving clinical decision with cloud support.

Hui Ma, Xuyang Guo, Yuan Ping, Baocang Wang, Yuehua Yang, Zhili Zhang, Jingxian Zhou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 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. 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

7 authors.

Hui MaSchool of Information Engineering, Xuchang University, Xuchang, Henan, China.
Xuyang GuoNo.1 Middle School of Zhengzhou, Zhengzhou, Henan, China.
Yuan PingSchool of Information Engineering, Xuchang University, Xuchang, Henan, China.ORCID 0000-0001-7703-4637
Baocang WangSchool of Information Engineering, Xuchang University, Xuchang, Henan, China.
Yuehua YangSchool of Information Engineering, Xuchang University, Xuchang, Henan, China.
Zhili ZhangSchool of Information Engineering, Xuchang University, Xuchang, Henan, China.
Jingxian ZhouInformation Technology Research Base of Civil Aviation Administration of China, Civil Aviation University of China, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the prosperity of machine learning and cloud computing, meaningful information can be mined from mass electronic medical data which help physicians make proper disease diagnosis for patients. However, using medical data and disease information of patients frequently raise privacy concerns. In this paper, based on single-layer perceptron, we propose a scheme of privacy-preserving clinical decision with cloud support (PPCD), which securely conducts disease model training and prediction for the patient. Each party learns nothing about the other's private information. In PPCD, a lightweight secure multiplication is presented and introduced to improve the model training. Security analysis and experimental results on real data confirm the high accuracy of disease prediction achieved by the proposed PPCD without the risk of privacy disclosure.

Indexed as

Decision Making, Computer-AssistedAlgorithmsCloud ComputingComputer SecurityConfidentialityDecision MakingDisclosureElectronic Health RecordsHumansMachine LearningMedical RecordsPrivacy

Identifiers

PMID31141561
PMCPMC6541381

What OpenQuestion holds

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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.