PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 16104320

[Some technical problems in setting up hospital PACS].

Qing-hua He·Jian-hua Hu·Xiao-guang Ou·Jing Wu·Zhi-xiong Huang
View original source ↗Export metadata ↓

Abstract

Having introduced PACS in brief, this paper presents some technical problems such as the selection of scale, function, database and monitor. And many solutions for these problems are given and compared with each other.

Explore related subjects

Data CollectionEquipment DesignHospital Information SystemsImage Processing, Computer-AssistedInformation Storage and RetrievalInternetRadiology Information SystemsSystems IntegrationEnglish AbstractJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Qing-hua He, Jian-hua Hu, Xiao-guang Ou, Jing Wu, Zhi-xiong Huang. 2002. [Some technical problems in setting up hospital PACS].. https://pubmed.ncbi.nlm.nih.gov/16104320/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

Probability estimation when some observations are grouped.

This paper considers the use of additional questions for decreasing survey non-response rates and an approach for estimating a probability based on the results obtained. In a survey, the respondents are asked to answer an original question and follow-up questions, where the answers for the follow-up questions are grouped answers for the original question. For example, respondents are asked to provide an exact number of incidents, but in cases of 'Do not know' or 'Refuse' responses, they are subsequently asked to pick an answer from a less specific categorical scale. The new estimator obtains smaller variance asymptotically and does not depend on a distribution family. This method is applied to income questions in a survey regarding injury prevention and behaviours. Another application is survey data on intimate partner violence, where some amendments were applied for incorporating post-stratification weights and for using non-random grouping. For additional illustration, an example of parameter estimation on artificially generated data is presented.

Data Collection↗