AUTOMATIC DATA PROCESSING OF MEDICAL LANGUAGE

该项目的主要目标是制定方法， 自然医学语言的自动处理。 关键信息 的患者记录被嵌入到生成的自然语言文本中 在身体检查或来自各个医院实验室的报告中。 通过该信息精确检索患者数据的子集， 需要增加这个潜在信息库的科学价值 回顾性研究，包括药物作用研究，以及 教学目的。 这项技术的传播超出了一个有限的 这取决于开发能够学习的“智能”算法 高度特殊的语义模型和语言语法 在医学知识的特定微观领域中使用的语言。 开发的技术用于外科手术的自动编码 NCI/DCBD/LP的病理报告，成为问题的根源 医学词典学和医学语义建模中的实例 信息. 基于逻辑的软件平台（词典环境 软件）正在开发中，以便于操作非循环 有向图词典和医学词典学研究，“高 医学信息的“分辨率”表示和查询，以及 句法和语义规则的归纳和演变。 实验 用人工神经网络解决自然界中出现的问题 语言被执行。

The major objective of the project is the development of methods for automatic processing of natural medical language. Crucial information of patient records is embedded in natural language text generated during physical exams or in reports from various hospital laboratories. Precise retrieval of subsets of patient data via this information is needed to increase the scientific value of this latent information pool for retrospective studies, including studies of drug effects, and for teaching purposes. The spreading of this technology beyond a limited domain depends upon developing "intellegent" algorithms able to learn the highly particular semantic models and language syntax governing the language used in specific micro-domains of medical knowledge. Developed technology is utilized in the automatic encoding of Surgical Pathology reports for NCI/DCBD/LP, which become a source of problems and examples in medical lexicography and semantic modeling of medical information. A logic-based software platform (Lexicographic Environment Software) is under development to facilitate manipulation of acyclic directed graph dictionaries and research in medical lexicography, "high resolution" representation and query of medical information, and induction and evolution of syntactic and semantic rules. Experiments with artificial neural network solutions to problems arising in natural language were performed.