DATA MINING FOR HEALTHCARE DECISION SUPPORT

这个研究项目的广泛和长期目标是帮助 提高保健服务的质量和成本效益 通过运用创新的数学建模，设计高效的 和新颖的解决方案，并有效利用强大的新 计算技术，可以促进知识发现大 数据库。 虽然从计算机科学和统计学中提取的方法 学科传统上用于解决这个问题，有 探索结合能力的重大机会 方法来自运筹学学科， 传统的技术，如使用线性和非线性 用于改进神经网络设计的编程算法， 性能和改进遗传算法的元启发式搜索 设计和性能。 借鉴这些最近 发展，该项目旨在开发一个混合的计算机科学 和基于运筹学的大型数据库挖掘方法。 的 这类新方法的潜力将使用高性能 匹兹堡大学开发的高质量临床数据库 医疗中心，由保健政策署提供资金 与研究 这个数据库包含了病人的大量信息 社区获得性肺炎（CAP） 该项目的具体重点是解决以下问题： 预测CAP患者的死亡率。 肺炎是一种 重要的问题，因为它影响到一个重要的群体 导致需要昂贵的住院治疗的并发症， 是美国第六大死因 本研究提出 根据记录的结果预测住院患者的死亡率 在最初的病人-医生接触期间， 这种技术被称为概率信念网络。 该方法将 通过结合元启发式策略的特殊功能进行扩展 称为禁忌搜索，以帮助减少搜索过程的复杂性 在概率信念网络的设计和构建过程中。 禁忌搜索的其他功能，如分散搜索，路径搜索 重链接和概率禁忌搜索，促进了高效的 探索从数据中学习信念网络的搜索空间。 这类新的方法将在一个大的真实的数据上进行测试。 肺炎临床数据库，并与 许多以前应用过的机器学习方法 同样的问题。

The broad, long-term objective of this research project is to help improve the quality and cost-effectiveness of health care delivery through the use of innovative mathematical modeling, design of efficient and novel solution approaches, and effective use of the powerful new computing technologies that can facilitate knowledge discovery in large databases. While methods drawn from computer science and statistics disciplines have traditionally been used for this problem, there are significant opportunities for exploring the capabilities of combining methods drawn from the operations research discipline with the traditional techniques, such as using linear and nonlinear programming algorithms for improving neutral network design and performance, and metaheuristic search for improving genetic algorithm design and performance. Drawing on these recent developments, this project aims to develop a hybrid of computer science and operations research based methods of mining large databases. The potential of this new class of methods will be demonstrated using a high quality clinical database developed at the University of Pittsburgh Medical Center through funding from the Agency for Health Care Policy and Research. This database contains extensive information on patients with community-acquired pneumonia (CAP). The specific focus of this project is to address the problem of predicting patient mortality in the area of CAP. Pneumonia is an important problem to investigate because it affects a significant group of people, leads to complications requiring expensive hospitalizations, and is the sixth leading cause of death in the US. This study proposes to predict mortality of hospitalized patients based on findings recorded during the initial patient-physician encounter using a prediction technique known as probabilistic belief networks. This method will be extended by combining the special features of a metaheuristic strategy called tabu search to help reduce the complexity of the search process during the design and construction of probabilistic belief networks. Additional features of tabu search, such as scatter search, path relinking, and probabilistic tabu search, facilitate the efficient exploration of the search space for learning belief networks from data. This new class of methods will be tested on real data from a large clinical database on pneumonia, and compared with the capabilities of a number of machine learning methods that have previously been applied to the same problem.