DATA MINING AND MODEL BUILDING IN MEDICAL INFORMATICS

医疗信息学中的数据挖掘和模型构建

• 批准号: 2738630
• 负责人: BRUCE G. BUCHANAN
• 金额: $ 20.82万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-05-01 至 2002-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2738630
• 关键词: artificial intelligence; classification; computer assisted instruction; computer simulation; computer system design /evaluation; human data; informatics; information retrieval; model design /development; pneumonia

Our long-term goal is to assist biomedical scientists by extracting and codifying new knowledge from large biomedical databases routinely by computer. As large collections of data become more readily accessibly, the opportunities for discovering new information increase. We propose here to work toward this goal by extending our prior research on machine learning in two important directions: (1) codification of disparate pieces of knowledge into a coherent model (model building), and (2) discovery of new information in medical databases (data mining). Machine learning programs find classification rules (or decision trees or networks) that separate members of a target class from other individuals. They have emphasized predictive accuracy, with some attention to tradeoffs between accuracy and cost of errors or between accuracy and simplicity. We propose a framework in which these, and other, tradeoffs are explicit and the criteria by which tradeoffs are made are available for modification. We also include semantic considerations among the criteria to control the internal coherence of models. "Data mining" is a recently-coined term for using computers to explore large databases, with a goal of discovering new relationships but usually with no specific target defined at the outset. In addition to accuracy, simplicity, coherence, and cost, a program that purports to discover new relationships must be able to assess novelty. We propose to measure the extent to which proposed relationships are novel by comparing them against existing knowledge in the domain of discourse, and to look for unusual rules (and other relations) that would be very interesting if true. The computer program we are primarily building on, RL, is a knowledge- based learning program that learns classification rules from a collection of data. RL has been demonstrated to be flexible enough to allow guidance from prior knowledge, and powerful enough to learn publishable information for scientists working in several different domains. Both parts of the research will requires extending the RL system in new ways detailed in the research plan, which are consistent with the overall design philosophy of the present system. We will primarily work with data already collected on pneumonia patients with with which we have considerable. We will test the generality of the criteria used to evaluate models and discoveries with a Baynesian Net learning. We will test the generality of the generality of the criteria used to evaluate models and discoveries with Bayesian Net learning system, K2.

我们的长期目标是协助生物医学科学家提取和 定期从大型生物医学数据库中编纂新知识， 电脑随着大量数据的收集变得更加容易访问， 发现新信息的机会增加。我们提出 在这里，我们通过扩展我们先前对机器的研究来实现这一目标 学习的两个重要方向：（1）编纂不同的 将知识片段转化为连贯的模型（模型构建），以及（2） 在医学数据库中发现新信息（数据挖掘）。 机器学习程序找到分类规则（或决策树 或网络），其将目标类的成员与其他 个体他们强调预测的准确性， 注意准确性和错误成本之间的权衡， 准确和简单。我们提出了一个框架，其中这些， 其他，权衡是明确的，权衡的标准是 可供修改。我们还包括语义 控制内部一致性的标准中的考虑因素 模型 "数据挖掘"是一个最近创造的术语，用于使用计算机探索 大型数据库，目标是发现新的关系， 通常一开始没有明确的目标。除了 准确性，简单性，连贯性和成本，一个旨在 发现新的关系必须能够评估新颖性。我们建议 衡量拟议关系的新颖程度， 将它们与话语领域的现有知识进行比较， 并寻找不寻常的规则（和其他关系）， 有趣的是，如果是真的。 我们主要构建的计算机程序RL是一种知识- 基于学习的程序，从 收集数据。RL已被证明足够灵活， 允许从先前的知识指导，并强大到足以学习 为在几个不同领域工作的科学家提供可靠的信息 域.研究的这两个部分都需要扩展RL 系统在新的方式详细的研究计划，这是一致的 本系统的总体设计理念。我们将 主要研究已经收集的肺炎患者的数据， 我们有相当多的。我们将测试的一般性 用贝叶斯网络评价模型和发现的标准 学习我们将检验标准的普遍性 用于通过贝叶斯网络学习评估模型和发现 系统，K2.