Evidence map›Paper›PMID 22969273›Full record

ArticleJournal of biomedicine & biotechnology2012

A privacy-preserved analytical method for ehealth database with minimized information loss.

Ya-Ling Chen, Bo-Chao Cheng, Hsueh-Lin Chen, Chia-I Lin, Guo-Tan Liao, Bo-Yu Hou, Shih-Chun Hsu

Abstract read
In one paragraph

Article in Journal of biomedicine & biotechnology, 2012. 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

7 authors.

Ya-Ling ChenService Systems Technology Center, Industrial Technology Research Institute (ITRI), Hsinchu, Taiwan.
Bo-Chao Cheng
Hsueh-Lin Chen
Chia-I Lin
Guo-Tan Liao
Bo-Yu Hou
Shih-Chun Hsu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digitizing medical information is an emerging trend that employs information and communication technology (ICT) to manage health records, diagnostic reports, and other medical data more effectively, in order to improve the overall quality of medical services. However, medical information is highly confidential and involves private information, even legitimate access to data raises privacy concerns. Medical records provide health information on an as-needed basis for diagnosis and treatment, and the information is also important for medical research and other health management applications. Traditional privacy risk management systems have focused on reducing reidentification risk, and they do not consider information loss. In addition, such systems cannot identify and isolate data that carries high risk of privacy violations. This paper proposes the Hiatus Tailor (HT) system, which ensures low re-identification risk for medical records, while providing more authenticated information to database users and identifying high-risk data in the database for better system management. The experimental results demonstrate that the HT system achieves much lower information loss than traditional risk management methods, with the same risk of re-identification.

Indexed as

Computer SecurityConfidentialityDatabases, FactualElectronic Health RecordsUser-Computer InterfaceInformation Storage and RetrievalTaiwan

Identifiers

PMID22969273
PMCPMC3437311

What OpenQuestion holds

Textmetadata
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.