Name
Abstract | ||
|---|---|---|
This paper proposes a method for construction of classifiers for discharge summaries. First, morphological analysis is applied to a set of summaries and a term matrix is generated. Second, correspond analysis is applied to the classification labels and the term matrix and generates two dimensional coordinates. By measuring the distance between categories and the assigned points, ranking of key words will be generated. Then, keywords are selected as attributes according to the rank, and training example for classifiers will be generated. Finally learning methods are applied to the training examples. Experimental validation shows that random forest achieved the best performance and the second best was the deep learner with a small difference, but decision tree methods with many keywords performed only a little worse than neural network or deep learning methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICHI.2017.92 | 2017 IEEE International Conference on Healthcare Informatics (ICHI) |
Keywords | DocType | ISBN |
Discharge summary,text mining,classification,deep learning,random forest,decision tree,SVM,correspondence analysis | Conference | 978-1-5090-4882-3 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shusaku Tsumoto | 1 | 1820 | 294.19 |
| Tomohiro Kimura | 2 | 1 | 5.76 |
| Haruko Iwata | 3 | 27 | 12.48 |
| Shoji Hirano | 4 | 560 | 99.17 |