Difference between revisions of "Denecke and Bernauer AIME 2007"
PastStudents (talk | contribs) |
PastStudents (talk | contribs) |
||
| Line 16: | Line 16: | ||
[[File:Denecke.png]] | [[File:Denecke.png]] | ||
| + | |||
| + | The performance of the system on template filling is quite good achieving 81-95% precision and 83-97% recall. However, their evaluations are limited to three templates: hospitalization, state at discharge, and risk factors. Also, the performance of filling the template, hospitalization, is relatively much lower than the other two, but the authors does not give description about what is the unique characteristics of hospitalization though they show some error analysis. | ||
== Related Papers == | == Related Papers == | ||
[[Zhou et al ACM symposium on Applied Computing 2006]] | [[Zhou et al ACM symposium on Applied Computing 2006]] | ||
| − | |||
Latest revision as of 01:04, 1 October 2010
Citation
Kerstin Denecke and Jochen Bernauer. 2007. Extracting Specific MEdical Data Using Semantic Structures. Artificial Intelligence in Medicine, LNCS Vol. 4594/2007, 257-264.
Online version
Summary
The paper presents a medical information extraction system which extracts a variety of information from free text clinical records in German.
This system can process other languages with some modification of its language dependent components.
Their approach is based on automatic generation of semantic structures for free text. The system automatically map text to semantic structures.
The performance of the system on template filling is quite good achieving 81-95% precision and 83-97% recall. However, their evaluations are limited to three templates: hospitalization, state at discharge, and risk factors. Also, the performance of filling the template, hospitalization, is relatively much lower than the other two, but the authors does not give description about what is the unique characteristics of hospitalization though they show some error analysis.
