著者

高田 真吾 岡本 誠 柘野 浩史 原田 誠之 保﨑 泰弘 御舩 尚志 光延 文裕 谷崎 勝朗 新谷 憲治 原田 実根

出版者

岡山大学医学部附属病院三朝分院

雑誌

岡大三朝分院研究報告 (ISSN:09187839)

巻号頁・発行日

vol.71, pp.68-72, 2001-02-01

---

播種性血管内凝固症候群(DIC)を合併した全身性エリテマトーデス(SLE)を経験したので報告する。症例は73歳女性。64歳時慢性関節リウマチ(RA)と診断された。1999年1月食欲低下を訴え当科受診した。血小板減少、FDP高値、PT上昇等よりDIC発症を疑った。膠原病では凝固系の異常を認めるが、本症例では凝固系が完進しDICを来したと考えられた。本症例はリウマチ因子陽性であったが、朝のこわばり等典型的なRAの所見に乏しく他の膠原病の合併を疑い、腎障害、血小板減少、抗Sm抗体、抗核抗体陽性よりSLEと診断した。A case of disseminated intravascular coagulation (DIC) in a patient with systemic lupus erythematosus (SLE) was described. A 73-year-old female was diagnosed as havingrheumatoid arthritis when she was 64 years old. In Jan, 1999, the patient was admitted to our hospital with the complaint of loss of appetite. She was suspected of DIC because ofthrombocytopenia, increased fibrin degradation product and prolonged prothrombin test.Abnormality in coagulation system is recognized in collagen disease. In this case coagulation system was activated and DIC occurred.In this case rheumatoid factor was positive. But she was suspected of complicating other collagen disease because she was poor in typical characteristics of rheumatoid arthritis,such as morning stiffness. SLE was diagnosed on the basis of renal injury, thrombocytopenia, positive anti-Sm antibodyand positive antinuclaer antibody in this case.

著者

原田 実 水野 高宏

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.16, no.1, pp.85-93, 2001 (Released:2002-02-28)

参考文献数

15

被引用文献数

3 6

---

Up to now, the research on the automation of object-oriented analysis, especially extracting objectoriented design elements from the problem specification written in Japanese, has been continued in the Harada laboratory since 1993. As this first process, we have developed the semantic analysis system SAGE which could be practically useable both in the performance and in the accuracy. Given a dependency tree, where clauses constituting a sentence are related by dependency arcs, SAGE searches the EDR electronic dictionary, retrieves for any two clauses connected by a dependency arc the meaning of the principal word in each clause and the deep case between such two words, and assigns the probability of such meaning-case tuple. Then, SAGE constructs an interpretation tree by allocating such meaning-case tuple and its probability to each arc in the dependency tree. Next, SAGE searches for the allocation having the maximum of the overall evaluation value given by the sum of the probability of the allocated meaning and cases. Finally, SAGE converts the resulting interpretation tree into the set of semantic frames containing the information of each word and relations with other words. In developing the system, we achieved speed-up of the construction of the interpretation tree by reducing the search space with pruning useless meaning-case tuples and by using the branch and bound method. Moreover the accuracy improvement of the analysis was achieved by applying the following four methods: (A)in constructing the interpretation tree, assigning 0 probability to all the combination of word meanings with which there are no “case” information in the concept description dictionary, (B)using the experimental rules to presume the deep cases from the surface cases to each dependency between verb clauses, (C)improving the fitness of the sentences retrieved from corpus by using part of speech, and (D)decreasing the number of meaning candidates by using reading information. As a result, the average interpretation construction time of one sentence with nine clauses or less was 2 seconds on a PC with the Pentium III processor using 320MB memory. The correct answer rate of the meaning was 82.1%, and that of the case was 77.8%.