Linked Life Data

11079942

Source:http://linkedlifedata.com/resource/pubmed/id/11079942

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2001-1-10
pubmed:abstractText
Medical records usually incorporate investigative reports, historical notes, patient encounters or discharge summaries as textual data. This study focused on learning causal relationships from intensive care unit (ICU) discharge summaries of 1611 patients. Identification of the causal factors of clinical conditions and outcomes can help us formulate better management, prevention and control strategies for the improvement of health care. For causal discovery we applied the Local Causal Discovery (LCD) algorithm, which uses the framework of causal Bayesian Networks to represent causal relationships among model variables. LCD takes as input a dataset and outputs causes of the form variable Y causally influences variable Z. Using the words that occur in the discharge summaries as attributes for input, LCD output 8 purported causal relationships. The relationships ranked as most probable subjectively appear to be most causally plausible.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1531-605X
pubmed:author
pubmed:issnType
Print
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
542-6
pubmed:dateRevised
2008-11-20
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
Causal discovery from medical textual data.
pubmed:affiliation
Center for Biomedical Informatics, Intelligent Systems Program, University of Pittsburgh, USA. mani@cbmi.upmc.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, U.S. Gov't, Non-P.H.S.