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KNOWLEDGE MANAGEMENT FOR CLINICAL TRIAL ADVICE SYSTEMS

KNOWLEDGE MANAGEMENT FOR CLINICAL TRIAL ADVICE SYSTEMS
临床试验建议系统的知识管理
批准号:
3373683
负责人:
BRUCE G. BUCHANAN
金额:
$17.75万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
1985
资助国家:
美国
项目状态:
已结题
起止时间:
1985-09-01 至 1988-08-31

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中文摘要
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英文摘要
We propose medical computing research to explore methods of knowledge acquisition and knowledge base management for integrated advice systems, specifically in the area of randomized clinical trials. ONCOCIN, an ongoing NIH-supported research project to develop an advice system for clinical trials in oncology, will provide the initial development environment for these knowledge management tools. Our goal is to develop interactive tools of two major types: 1. Tools that will allow physicians both to design new research protocols and to enter predefined protocols directly into the computer, without needing to rely on a computer programmer (knowledge engineer) for assistance. The resulting knowledge base will then be used by the ONCOCIN program when it provides advice and data management support for patients on these new protocols. 2. Tools that will allow a knowledge engineer to maintain a knowledge base, explore its contents, encode specialized knowledge not able to be entered directly by the physician, and create new programs for use by experts wishing to enter experimental protocols in medical domains other than oncology. In developing the interactive tools for use by physician experts, we will develop three subprograms: (1.1) a knowledge acquisition system (known as OPAL) to allow oncologists to enter new protocol knowledge for ONCOCIN without assistance from computer scientists, (1.2) a facility for graphical review of existing protocol information, and (1.3) software for automatic generation of text documents that describe encoded protocols. These programs will permit rapid entry of new oncology protocols for ONCOCIN and will also serve as "computer aided design" tools for oncologists developing new protocols. In developing the interactive tools for use by knowledge engineers, we will also develop three subprograms: (2.1) a graphics-based editor for review and modification of protocol knowledge so that concepts may be entered even if they cannot be encoded using the knowledge acquisition system, (2.2) routines to permit extension of the knowledge acquisition system so that physicians may enter previously unspecifiable concepts, and (2.3) a system for creating entirely new knowledge acquisition systems for other types of clinical trials.
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DATA MINING AND MODEL BUILDING IN MEDICAL INFORMATICS
ARTIFICIAL INTELLIGENCE METHODS FOR CRYSTALIZATION
DATA MINING AND MODEL BUILDING IN MEDICAL INFORMATICS
ARTIFICIAL INTELLIGENCE METHODS FOR CRYSTALIZATION
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