
pmid: 16779318
pmc: PMC1560897
Incorporation of evidence from clinical research requires critical appraisal of its quality. Information retrieval systems can facilitate clinicians' judgments by automatically labeling retrieved citations with their strength of evidence categories. Preliminary results of such a text classification experiment involving MEDLINE citations show that a "bag of words" approach is insufficient for accurate classification.
Evidence-Based Medicine, Artificial Intelligence, MEDLINE, Humans, Information Storage and Retrieval, Bayes Theorem
Evidence-Based Medicine, Artificial Intelligence, MEDLINE, Humans, Information Storage and Retrieval, Bayes Theorem
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
