Name
Abstract | ||
|---|---|---|
Inductive learning algorithms have been proposed as methods for classing medical text reports. Many of these proposed techniques differ in the way the text is represented for use by the learning algorithms. Slight differences can occur between representations that may be chosen arbitrarily, but such differences can significantly affect classification algorithm performance. We examined 8 different data representation techniques used for medical text, and evaluated their use with standard machine learning algorithms. We measured the loss of classification-relevant information due to each representation. Representations that captured status information explicitly resulted in significantly better performance. Algorithm performance was dependent on subtle differences in data representation. |
Year | Venue | Field |
|---|---|---|
2000 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION | External Data Representation,Multi-task learning,Inductive transfer,Computer science,Generalization error,Natural language processing,Records as Topic,Artificial intelligence,Machine learning |
DocType | Issue | ISSN |
Conference | SUPnan | 1067-5027 |
Citations | PageRank | References |
12 | 1.65 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adam Wilcox | 1 | 215 | 35.66 |
| George Hripcsak | 2 | 1493 | 160.86 |