EXPERT SYSTEM IN THE DIAGNOSIS OF LYMPHOMA AND LEUKEMIA

淋巴瘤和白血病诊断专家系统

• 批准号: 2555455
• 负责人: EILEEN K STEINBERGER
• 金额: $ 5.25万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-03-31 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/2555455
• 关键词: artificial intelligence; blood disorder diagnosis; computer assisted diagnosis; computer assisted medical decision making; computer data analysis; data management; diagnosis design /evaluation; human data; information systems; leukemia; lymphoma; mathematical model; neoplasm /cancer diagnosis

The diagnosis of hematologic malignancies requires many qualitative ancillary tests to substantiate the impressions made from hematoxylin and eosin stained slides. We propose to use artificial intelligence techniques to expedite and integrate evaluation of information received form histologic morphology, special chemical and enzymatic stains, immunoperoxidase stains, flow cytometry, and molecular diagnostic studies. The goals for this fellowship include: 1) design and implement a database of the important clinical laboratory variables relevant to the diagnosis of hematologic malignancies, 2) develop an expert system based on knowledge-based rules derived from the medical literature and from internal clinical material, and 3) validate the system with retrospective and prospective clinical data. To accomplish these goals and to build this expert system, techniques from two major disciplines in computer science will be applied. First, the relational model of database management will be used to store and retrieve clinical data. Second, artificial intelligence techniques such as predicate calculus, case-based reasoning, neural networks, and fuzzy logic will be used to develop this expert system.

恶性血液病的诊断需要许多定性的 辅助试验，以证实苏木素印模， 伊红染色的载玻片。 我们建议使用人工智能 加快和综合评价所收到的资料的技术 形成组织形态学、特殊的化学和酶染色， 免疫过氧化物酶染色、流式细胞术和分子诊断研究。 该奖学金的目标包括：1）设计和实施一个 重要临床实验室变量数据库 诊断血液恶性肿瘤，2）开发一个专家系统， 基于知识的规则来自医学文献和 内部临床资料，以及3）通过回顾性分析验证系统 和前瞻性临床数据。 为了实现这些目标， 这个专家系统，技术来自计算机两大学科 科学将被应用。 一、数据库的关系模型 管理将用于存储和检索临床数据。 第二、 人工智能技术，如谓词演算，基于案例的 推理、神经网络和模糊逻辑将用于开发这一系统 专家系统