Linked Life Data

11310158

Source:http://linkedlifedata.com/resource/pubmed/id/11310158

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2001-4-19
pubmed:abstractText
Missing data are a major plague of medical databases in general, and of Intensive Care Unit databases in particular. The time pressure of work in an Intensive Care Unit pushes the physicians to omit randomly or selectively record data. These different omission strategies give rise to different patterns of missing data and the recommended approach of completing the database using median imputation and fitting a logistic regression model can lead to significant biases. This paper applies a new classification method, called robust Bayes classifier, which does not rely on any particular assumption about the pattern of missing data and compares it to the median imputation approach using a database of 324 Intensive Care Unit patients.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
0026-1270
pubmed:author
pubmed:issnType
Print
pubmed:volume
40
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
39-45
pubmed:dateRevised
2004-11-17
pubmed:meshHeading
pubmed:year
2001
pubmed:articleTitle
Robust outcome prediction for intensive-care patients.
pubmed:affiliation
Children's Hospital Informatics Program, Harvard Medical School, Boston MA, USA. marco_ramoni@harvard.edu
pubmed:publicationType
Journal Article