Application of Active Mining to Prevention of Medical Incidents and Accidents

主动挖掘在预防医疗事故和事故中的应用

• 批准号: 17300048
• 负责人: TSUMOTO Shusaku
• 金额: $ 5.95万
• 依托单位: Shimane University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 2005
• 资助国家: 日本
• 起止时间: 2005 至 2006
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-17300048/
• 关键词: Active Mining; Data Mining; Hospital Information System; Risk Discovery; Medical Incidents; 医療事故防止; インシデント

Reports on medical incidents and accidents have been stored for their analysis and prevention. This study aims at developing a system to analyze stored data by using active mining technology and to clarify the hidden factors behind these incidents. In 2005, we developed a data-warehouse system which stores incident reports electronically. In this fiscal year, based on the stored data, we apply several data mining methods to stored data and the data related with incidents, extracted from hospital information system (HIS) as follows. In the upper half of 2006, we developed a data-warehouse server which combines incident data with data related with incidents, which are extracted from HIS. In the latter half of 2006, data mining methods were applied to data in the data-warehouse server. The results obtained from this analysis are the following: (1) While about 7000 actions are performed and registered in the university hospital, 120 cases were registered as medical incidents, 10 cases a month. Thus, the probability of medical accidents is at most 1 to 20000. (2) Age of a patient is one of the principal factor for accidents. (3) diagnosis departments are not so related with incidents. Although data is imbalanced, several interesting results were observed from extracted patterns.

已存储医疗事件和事故的报告，以供分析和预防。本研究旨在开发一个利用主动挖掘技术分析存储数据的系统，并澄清这些事件背后的隐藏因素。2005年，我们开发了一个以电子方式存储事件报告的数据仓库系统。在本财年，我们基于存储的数据，将几种数据挖掘方法应用于存储的数据和从医院信息系统(HIS)中提取的与事件相关的数据，如下所示。2006年上半年，我们开发了一个数据仓库服务器，将事件数据与从HIS中提取的与事件相关的数据结合在一起。2006年下半年，对数据仓库服务器中的数据应用了数据挖掘方法。分析结果如下：(1)在高校医院开展登记的7000余例中，登记为医疗事故的120例，每月10例。因此，发生医疗事故的概率至多为1%至20000。(2)患者年龄是造成事故的主要因素之一。(3)诊断科与事件的关联度不高。尽管数据是不平衡的，但从提取的模式中观察到了一些有趣的结果。

项目成果

複合医工学としてのデータマイニング

数据挖掘作为复杂的医学工程

• 发表时间: 2007
• 期刊: 人工知能学会誌 21・2
• 作者: 津本周作;平野章二
• 通讯作者: 平野章二

Visualization of Semantic Similarities and Syntactic Similarities of Rules using Multidimensional Scaling

使用多维尺度可视化规则的语义相似性和句法相似性

• 发表时间: 2007
• 期刊: Fundamenta Informaticae 78
• 作者: Tsumoto;S.;Hirano;S.
• 通讯作者: S.

ダータマイニングの基礎

数据挖掘基础知识

• 发表时间: 2006
• 作者: 元田浩;津本周作;山口高平;沼尾正行
• 通讯作者: 沼尾正行

Automated discovery of chronological patterns in long time-series medical datasets.

自动发现长时间序列医学数据集中的时间顺序模式。

• 发表时间: 2005
• 期刊: Journal of Intelligent System 20・6
• 作者: Shusaku Tsumoto;Shoji Hirano
• 通讯作者: Shoji Hirano

Visualization of Similarities and Dissimilarities in Rules Using Multidimensional Scaling

• DOI: 10.1007/11425274_4
• 发表时间: 2005-05
• 作者: S. Tsumoto;S. Hirano