1996
DOI: 10.1006/cbmr.1996.0026
|View full text |Cite
|
Sign up to set email alerts
|

Development and Evaluation of a Computerized Admission Diagnoses Encoding System

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
22
0

Year Published

2000
2000
2015
2015

Publication Types

Select...
4
3
1

Relationship

0
8

Authors

Journals

citations
Cited by 37 publications
(23 citation statements)
references
References 10 publications
1
22
0
Order By: Relevance
“…Although completely unsupervised, this approach is limited by the index not being able to capture all synonymous occurrences and also the inability to code both specific exclusions and other condition specific guidelines. Gunderson et al [5] extracted ICD-9 codes from short free text diagnosis statements that were generated at the time of patient admission using a Bayesian network to encode semantic information. However, in the recent past, concept extraction from longer documents such as discharge summaries has gained interest.…”
Section: Related Work and Backgroundmentioning
confidence: 99%
“…Although completely unsupervised, this approach is limited by the index not being able to capture all synonymous occurrences and also the inability to code both specific exclusions and other condition specific guidelines. Gunderson et al [5] extracted ICD-9 codes from short free text diagnosis statements that were generated at the time of patient admission using a Bayesian network to encode semantic information. However, in the recent past, concept extraction from longer documents such as discharge summaries has gained interest.…”
Section: Related Work and Backgroundmentioning
confidence: 99%
“…9,10 SymText was developed to encode information in chest x-ray reports but has been used for admission diagnoses and ventilation/perfusion lung scan reports. 13,14 The underlying structure has been previously described. 9,10 SymText has a syntactic and a semantic component.…”
Section: Subjects and Gold Standardmentioning
confidence: 99%
“…Although completely unsupervised, this approach is limited by the index not being able to capture all synonymous occurrences and also the inability to code both specific exclusions and other condition specific guidelines. Gunderson et al [4] extracted ICD-9 codes from short free text diagnosis statements that were generated at the time of patient admission using a Bayesian network to encode semantic information. However, in the recent past, concept extraction from longer documents such as discharge summaries has gained interest.…”
Section: Related Workmentioning
confidence: 99%