著者

岡本 和也 内山 俊郎 竹村 匡正 足立 貴行 粂 直人 黒田 知宏 内山 匡 吉原 博幸

出版者

公益社団法人 日本生体医工学会

雑誌

生体医工学 (ISSN:1347443X)

巻号頁・発行日

vol.49, no.1, pp.40-47, 2011-02-10 (Released:2011-12-13)

参考文献数

30

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A DPC code expresses a primary disease, a complication, and procedures, etc. In 2010, 1334 hospitals use DPC codes for calculations of medical fees. Since, in the hospitals, the medical fee of each case is calculated based on one DPC code, each case must be classified into one DPC code. However, the classification is difficult in some cases because patients sometimes have various conditions. Therefore, automatic DPC code selections using machine learning are being studied. Suzuki et al. evaluated automatic DPC code selections from discharge summaries using a vector space method. However, there are general machine learning methods except for the vector space method. Hence, we must evaluate other machine learning methods exhaustively for improvement of accuracy of automatic DPC code selections. Therefore, we evaluated automatic DPC code selections from discharge summaries using naïve Bayes method, SVM, concept base method, and another vector space method which is different from the vector space model used by Suzuki et al. We considered these machine learning methods as general ones. We also focus on characteristics of each machine learning methods on automatic DPC code selections and we utilize a method which combines some machine learning methods. First, the combining method estimates confidences of the machine learning methods bases on classification scores that the machine learning methods regard as classification evidence. Next, the combining method adopts the method whose confidence is highest. We compared accuracy of the methods using discharge summaries created in 2008 fiscal year in Kyoto University Hospital. As a result, SVM classified 72.2% of the cases into correct DPC codes though the vector space model utilized by Suzuki et al. classified 64.8% into correct DPC codes. Moreover the combining method classified 76.1% into correct DPC codes. In conclusion, we achieved significant improvement.

著者

佐藤 大祐 松林 達史 足立 貴行 大井 伸哉 田中 悠介 長野 翔一 六藤 雄一 塩原 寿子 宮本 勝 戸田 浩之

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.35, no.2, pp.D-wd05_1-10, 2020-03-01 (Released:2020-03-01)

参考文献数

16

被引用文献数

2

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In places where many people gather, such as large-scale event venues, it is important to prevent crowd accidentsfrom occurring. To that end, we must predict the flows of people and develop remedies before congestioncreates a problem. Predicting the movement of a crowd is possible by using a multi-agent simulator, and highly accurateprediction can be achieved by reusing past event information to accurately estimate the simulation parameters.However, no such information is available for newly constructed event venues. Therefore, we propose here a methodthat improves estimation accuracy by utilizing the data measured on the current day. We introduce a people-flowprediction system that incorporates the proposed method. In this paper, we introduce results of an experiment on thedeveloped system that used people flow data measured at an actual concert event.