EXPERT SYSTEM IN THE DIAGNOSIS OF LYMPHOMA AND LEUKEMIA

恶性血液病的诊断需要许多定性的 辅助测试以证实苏木精制成的印迹 伊红染色的玻片。我们建议使用人工智能 加快和综合评估收到的信息的技术 从组织学形态，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 与临床实验室相关的重要变量数据库 恶性血液病的诊断，2)开发一个基于 从医学文献和从知识到知识的规则 内部临床资料，以及3)通过回顾验证系统 和前瞻性的临床数据。为了实现这些目标并建立 这个专家系统，来自计算机两个主要学科的技术 科学将会被应用。首先，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，如基于案例的谓词演算 推理、神经网络和模糊逻辑将被用来发展这一点 专家系统。

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.