Linked Life Data

10906612

Source:http://linkedlifedata.com/resource/pubmed/id/10906612

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2000-9-14
pubmed:abstractText
The high incidence of false alarms in the intensive care unit (ICU) necessitates the development of improved alarming techniques. This study aimed to detect artifact patterns across multiple physiologic data signals from a neonatal ICU using decision tree induction. Approximately 200 h of bedside data were analyzed. Artifacts in the data streams were visually located and annotated retrospectively by an experienced clinician. Derived values were calculated for successively overlapping time intervals of raw values, and then used as feature attributes for the induction of models trying to classify 'artifact' versus 'not artifact' cases. The results are very promising, indicating that integration of multiple signals by applying a classification system to sets of values derived from physiologic data streams may be a viable approach to detecting artifacts in neonatal ICU data.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
0933-3657
pubmed:author
pubmed:issnType
Print
pubmed:volume
19
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
189-202
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
Multiple signal integration by decision tree induction to detect artifacts in the neonatal intensive care unit.
pubmed:affiliation
MIT Laboratory for Computer Science, Cambridge, MA 02139, USA. chris@medg.lcs.mit.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't