Name
Abstract | ||
|---|---|---|
This paper proposes a method which classifies discharge summaries stored in hospital information system, which consists of the following four steps. First, a term matrix of the set of summaries is induced by morphological analysis (RMecab). Next, correspondence analysis is applied to the term matrix and numerical values of two dimensional coordinates are assigned to each keyword and each concept. By measuring the euclidean distance between categories and keywords, keywords are ordered. Then, keywords are selected as attributes according to the rank, and training examples for classifiers will he generated. Finally, learning methods are applied to the training examples. Experimental validation shows that random forest achieved the hest performance and deep learning (multiple layer perceptron) is the second best. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BigData47090.2019.9006296 | 2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) |
Keywords | Field | DocType |
Discharge summary, text mining, classification, deep learning, random forest, decision tree, SVM, correspondence analysis | Data mining,Decision tree,Computer science,Matrix (mathematics),Support vector machine,Euclidean distance,Artificial intelligence,Deep learning,Correspondence analysis,Random forest,Perceptron | Conference |
ISSN | Citations | PageRank |
2639-1589 | 0 | 0.34 |
References | Authors | |
0 | 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 |