NDLI: A framework for dynamic evidence based medicine using data mining

Content Provider	IEEE Xplore Digital Library
Author	Masuda, G. Sakamoto, N.
Copyright Year	2002
Description	Author affiliation: Div. of Med. Informatics, Osaka Med. Coll., Japan (Masuda, G.)
Abstract	Dynamic evidence-based medicine (DEBM) is defined as the process of finding evidence about the care of individual patients automatically and dynamically in those cases when we cannot rely on any literature or guidelines. In this paper, we develop a framework for DEBM using data mining technologies that make it possible to automatically analyze huge clinical databases and to discover patterns behind them. We define the requirements of a data mining system for DEBM. The following functions are required of the system: (1) support for clinical decision making, and (2) discovery of rare patterns which human beings can hardly find. In order to support clinical decision making, rule discovery methods such as association rule mining are applied to this framework. We adopt a post-analysis approach using a rule base and queries. The discovered rules are collected into a rule base for further analysis. By submitting queries to the rule base, users can obtain keys to evidence for making decisions about clinical care. We preliminarily implement a prototype of a rule base and a post-analysis tool based on our framework. This tool can assist users in analyzing the discovered rules.
Sponsorship	IEEE Comput. Soc. Tech. Committee on Comput. Med. Univ. Maribor, Faculty of Electr. Eng. & Comput. Sci
Starting Page	117
Ending Page	122
File Size	372739
Page Count	6
File Format	PDF
ISBN	0769516149
ISSN	10637125
DOI	10.1109/CBMS.2002.1011364
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-06-04
Publisher Place	Slovenia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Databases Biomedical informatics Data analysis Decision making Diseases Educational institutions Guidelines Pattern analysis Humans
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Computer Science Applications

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