| Presentation | 2015-09-11 Feature Words to Predict Long Post-Operatively Stay in Semi-structured Medical Records Takanori Yamashita, Haruka Kubo, Yuusuke Adachi, Yoshifumi Wakata, Brendan Flanagan, Hidehisa Soejima, Naoki Nakashima, Sachio Hirokawa, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The hospital information system has expanded, and importance of medical data utilization is increasing. Electronic medical data include structured digital data and unstructured text data. It is expected that analyzing unstructured text data contribute to clinical decision support and improve medical process. The present paper analyzes the words that appear in semi-structured medical chart to predict the postoperative length of stay. SVM (support vector machine) were applied to extract feature words that characterize the medical charts of patients who stayed longer than the standard hospitalization. Three measures were proposed and the prediction performance for importance of the feature word was evaluated with respect to each measure. We extract the words that related clinical test, and show high frequency of the words in Objective item. Furthermore, we apply to show feature words that characterize the medical charts of long-hospitalization by Decision tree. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | medical chart / POMR / SVM(support vector machine) / Decision Tree |
| Paper # | NLC2015-32 |
| Date of Issue | 2015-09-03 (NLC) |
| Conference Information | |
| Committee | NLC |
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| Conference Date | 2015/9/10(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Convention Room AP Shibuya-Dogenzaka (Tokyo) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | The Seventh Text Mining Symposium |
| Chair | Koichi Takeuchi(Okayama Univ.) |
| Vice Chair | Hiroshi Kanayama(IBM) / Makoto Ichise(NTT DoCoMo) |
| Secretary | Hiroshi Kanayama(Univ. of Tokyo/Hottolink) / Makoto Ichise(Ryukoku Univ.) |
| Assistant | Kazutaka Shimada(Kyushu Inst. of Tech.) / Ryuichiro Higashinaka(NTT) |
| Paper Information | |
| Registration To | Technical Committee on Natural Language Understanding and Models of Communication |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Feature Words to Predict Long Post-Operatively Stay in Semi-structured Medical Records |
| Sub Title (in English) | |
| Keyword(1) | medical chart |
| Keyword(2) | POMR |
| Keyword(3) | SVM(support vector machine) |
| Keyword(4) | Decision Tree |
| 1st Author's Name | Takanori Yamashita |
| 1st Author's Affiliation | Kyushu University(Kyushu Univ.) |
| 2nd Author's Name | Haruka Kubo |
| 2nd Author's Affiliation | Kyushu University(Kyushu Univ.) |
| 3rd Author's Name | Yuusuke Adachi |
| 3rd Author's Affiliation | Kyushu University(Kyushu Univ.) |
| 4th Author's Name | Yoshifumi Wakata |
| 4th Author's Affiliation | Kyushu University(Kyushu Univ.) |
| 5th Author's Name | Brendan Flanagan |
| 5th Author's Affiliation | Kyushu University(Kyushu Univ.) |
| 6th Author's Name | Hidehisa Soejima |
| 6th Author's Affiliation | Saiseikai Kumamoto Hospital(Kumamoto Hospital) |
| 7th Author's Name | Naoki Nakashima |
| 7th Author's Affiliation | Kyushu University(Kyushu Univ.) |
| 8th Author's Name | Sachio Hirokawa |
| 8th Author's Affiliation | Kyushu University(Kyushu Univ.) |
| Date | 2015-09-11 |
| Paper # | NLC2015-32 |
| Volume (vol) | vol.115 |
| Number (no) | NLC-222 |
| Page | pp.pp.75-79(NLC), |
| #Pages | 5 |
| Date of Issue | 2015-09-03 (NLC) |