医学知识库与医学知识的获取

本文概述了医学知识的来源、医学知识库的构成及其知识表达；阐述了知识获取的方式和一般步骤；对医学知识库建设中需要解决的几个关键问题进行了分析和探讨。     

Protégé在构建中医治则治法本体中的运用

本文探讨了中医治则治法的逻辑框架；初步建立了中医治则治法的本体，讨论了其与高成勉等的中医学顶层本体的关系；在此基础上探索了利用Protege3．3建立本体的方法；最后本文还对中医治则治法的形式化作了原则性的探索。     

医学文献检索教材建设与提高该课教学质量的探讨

本文通过对医学文献检索教材建设的现状及特点加以分析,认为教材建设速度太慢,且内容陈旧,跟不上当今医学知识五年一更新的速度,更跟不上计算机技术突飞猛进的发展,严重影响了该课程的教学质量。提出了解决此问题的方法及措施,并提出了医学文献检索课教师应具备的素质和技能。     

肺癌分子标志物的研究进展及转化应用

转化医学促进了分子生物学等基础医学发展,改变了医学模式。其目的是将医学的基础研究成果快速有效的转化为用于临床的技术和药物,为医学知识服务临床铺平道路。随着肿瘤分子生物学的研究进展,越来越多的分子生物标志物被发现并用于肺癌的诊断、治疗、预后和疗效监测。本文将肺癌分子标志物的研究进展和在转化方面的应用进行综述。     

网络信息与医学文献检索

信息已成为世界各个国家促进经济发展最重要的战略资源之一,信息时代的网上资源极其丰富,迅速而准确的获取网络信息资源是所有科技工作者和高校医学生必须掌握的知识。医学科技进步提供了成千上万的医学文献,应用网络信息进行医学文献检索对掌握医学知识,促进医学科技发展,提高医学水平均具有重要的意义。本文介绍网络信息特点,网络信息分布,医学信息资源网上检索,网络医学文献检索方法等,以供读者参考。     

论医学知识标准化的复杂性和HCSL进展

本文讨论了以自由文本为基础的医学知识的社会性、物理性和非本体性及其所致的标准化工作的复杂性，进而描述了一种标准化医学语言即半自然半结构的医学语言HCSL在结构、成分、序性、运算性、表达性等方面的进展。     

医学知识工作者在知识管理中的作用

知识管理是以“知识为基础”的管理，管理的对象是各种知识和与知识有关的技术，组织结构及其它各种资源，其中知识工作者是最活跃的因素。医学图书馆员成为知识资源与需要知识的用户之间的桥梁。通过医学知识工作者的知识服务使医学图书馆功能得以扩大和增强。     

元素化、关系化、网联化、智能化的医学知识新媒体——IRMEB

本文试析了当前促成提出整合性关系化医学电子书（ＩＲＭＥＢ）的医学知识爆炸的成因，以及泛系方法论对ＩＲＭＥＢ的影响，进一步描述了ＩＲＭＥＢ的最新进展、结构、以及它的一些绝无仅有的功能。本文还探讨了ＩＲＭＥＢ的极期广阔的开发前景、潜在的问题及戎相应的对策     

乌鲁木齐市汉族、维吾尔族医学生对全科医学需求的调查研究

目的 调查乌鲁木齐市某医学院校汉族及维吾尔族医学生对全科医学的现状及需求情况,进一步了解不同民族医学生是否有民族差异性。方法 2016年5月~2017年5月选取某医学院校2013级本科汉族及维吾尔族在校医学生为调查对象,对全科医学教育及知识掌握需求情况、实习需求情况分别进行调查,采用整群随机抽样方法抽取研究对象,自行设计并进行问卷调查,并进行统计学分析。结果 2013级汉族及维吾尔族医学生总共发放303份调查问卷,回收有效问卷283份,有效回收率93.40%,有228名医学生认为有必要开展社区实习,其中185名医学生愿意到社区实习,有265名医学生了解全科医学知识,认为全科医学就业前景好的有69名学生,汉族与维吾尔族医学生对全科医学教育现状需求差异有统计学意义（P＜0.05）,2013级汉族及维吾尔族医学生对全科医学知识掌握及需求情况,被调查学生对健康管理及健康教育、慢性疾病、内科系统及妇科诊疗知识掌握情况较好,医学生对全科医学实习需求情况,有261名医学生认为开展实践以临床实践较为合理。其中199名医学生愿意从事社区卫生服务工作,汉族与维吾尔族医学生差异有统计学意义（P＜0.05）。结论 在校本科医学生大部分愿意从事社区卫生服务工作,了解全科医学知识,对全科医学的知识及需求较高。但有民族差异性,维吾尔族医学生较汉族医学生更愿意前往社区实习,更多名医学生了解全科医学知识并对全科医学就业前景预期较高。     

以整体医学观指导《实用人体解剖学》专著的编撰

整体医学观是多器官受累的慢病时代的医学新观点,是医学由经验医学和实验医学两个时代进入整体医学 时代的显著标志。现结合中国医科大学30 年人体形态科学整合教学的经验,以主编的《实用人体解剖学》专著 的撰写为范例,阐述整体医学时代人体解剖学著作撰写的方法。以人体解剖学及与其密切相关的学科进行知识融 合,体现整合医学特点。铸造人体结构整合医学知识链,达到人体知识的易懂、易学、易记和触类旁通。践行现 代医学教材的编写应体现整合医学的思想。     

基于医学本体的临床路径知识库模型建设

目的建立临床路径知识库模型,提高临床路径软件的自适应性,提升电子病历应用水平。方法利用本体知识库编辑工具,从医院已有的临床路径病种数据应用出发,对临床知识库内容进行规范化研究和标准化表达,形成临床路径知识库模型。结果建立了58个病种的临床路径知识库,该知识库描述了医院临床路径内容的各个方面,可应用于新一代临床信息系统建设。结论临床路径本体知识库对智能化电子病历应用和临床决策支持系统有重要作用,有利于医院信息系统的语义集成,是新一代电子病历应用的重要组成部分。     

Conceptual, Experimental and Compass-beacon Ontologies——The Principles, concepts and methodology of BMKI（Ⅰ）

1 Starting from Inform ation U nit(IU )Radically speaking, any inform ation unit(IU ) needs toansw er these questions: ① W hat object w e are going toknow ? ②W hich aspects of the object w e have chosen tobe observed? ③W hat is the result of the obser…     

An ontological model of the practice transformation process

Patient-centered medical home is defined as an approach for providing comprehensive primary care that facilitates partnerships between individual patients and their personal providers. The current state of the practice transformation process is ad hoc and no methodological basis exists for transforming a practice into a patient-centered medical home. Practices and hospitals somehow accomplish the transformation and send the transformation information to a certification agency, such as the National Committee for Quality Assurance, completely ignoring the development and maintenance of the processes that keep the medical home concept alive. Many recent studies point out that such a transformation is hard as it requires an ambitious whole-practice reengineering and redesign. As a result, the practices suffer change fatigue in getting the transformation done. In this paper, we focus on the complexities of the practice transformation process and present a robust ontological model for practice transformation. The objective of the model is to create an understanding of the practice transformation process in terms of key process areas and their activities. We describe how our ontology captures the knowledge of the practice transformation process, elicited from domain experts, and also discuss how, in the future, that knowledge could be diffused across stakeholders in a healthcare organization. Our research is the first effort in practice transformation process modeling. To build an ontological model for practice transformation, we adopt the Methontology approach. Based on the literature, we first identify the key process areas essential for a practice transformation process to achieve certification status. Next, we develop the practice transformation ontology by creating key activities and precedence relationships among the key process areas using process maturity concepts. At each step, we employ a panel of domain experts to verify the intermediate representations of the ontology. Finally, we implement a prototype of the practice transformation ontology using Protégé.     

A methodology for extending domain coverage in SemRep

We describe a domain-independent methodology to extend SemRep coverage beyond the biomedical domain. SemRep, a natural language processing application originally designed for biomedical texts, uses the knowledge sources provided by the Unified Medical Language System (UMLS^©). Ontological and terminological extensions to the system are needed in order to support other areas of knowledge. We extended SemRep’s application by developing a semantic representation of a previously unsupported domain. This was achieved by adapting well-known ontology engineering phases and integrating them with the UMLS knowledge sources on which SemRep crucially depends. While the process to extend SemRep coverage has been successfully applied in earlier projects, this paper presents in detail the step-wise approach we followed and the mechanisms implemented. A case study in the field of medical informatics illustrates how the ontology engineering phases have been adapted for optimal integration with the UMLS. We provide qualitative and quantitative results, which indicate the validity and usefulness of our methodology.     

Building an ontology of pulmonary diseases with natural language processing tools using textual corpora

Pathologies and acts are classified in thesauri to help physicians to code their activity. In practice, the use of thesauri is not sufficient to reduce variability in coding and thesauri are not suitable for computer processing. We think the automation of the coding task requires a conceptual modeling of medical items: an ontology. Our task is to help lung specialists code acts and diagnoses with software that represents medical knowledge of this concerned specialty by an ontology. The objective of the reported work was to build an ontology of pulmonary diseases dedicated to the coding process. To carry out this objective, we develop a precise methodological process for the knowledge engineer in order to build various types of medical ontologies. This process is based on the need to express precisely in natural language the meaning of each concept using differential semantics principles. A differential ontology is a hierarchy of concepts and relationships organized according to their similarities and differences. Our main research hypothesis is to apply natural language processing tools to corpora to develop the resources needed to build the ontology. We consider two corpora, one composed of patient discharge summaries and the other being a teaching book. We propose to combine two approaches to enrich the ontology building: (i) a method which consists of building terminological resources through distributional analysis and (ii) a method based on the observation of corpus sequences in order to reveal semantic relationships. Our ontology currently includes 1550 concepts and the software implementing the coding process is still under development. Results show that the proposed approach is operational and indicates that the combination of these methods and the comparison of the resulting terminological structures give interesting clues to a knowledge engineer for the building of an ontology.     

HL7 ontology and mobile agents for interoperability in heterogeneous medical information systems

Modern medical information management is a knowledge intensive activity requiring a high degree of interoperability across various health management entities. Ontology-based multi-agent systems provide a framework for interactions in a distributed medical systems environment without the limitations of a more traditional client server approach. In this paper, we describe electronic Medical Agent System (eMAGS) a multi-agent system with an ontology based on an accepted public health message standard, Health Level Seven (HL7), to facilitate the flow of patient information across a whole healthcare organisation.     

Context-based ontology building support in clinical domains using formal concept analysis

OBJECTIVE: Ontology in clinical domains is becoming a core research field in the realm of medical informatics. The objective of this study is to explore the potential role of formal concept analysis (FCA) in a context-based ontology building support in a clinical domain (e.g. cardiovascular medicine here). METHODOLOGY: We developed an ontology building support system that integrated an FCA module with a natural language processing (NLP) module. The user interface of the system was developed as a Protégé-2000 JAVA tab plug-in. A collection of 368 textual discharge summaries and a standard dictionary of Japanese diagnostic terms (MEDIS ver2.0) were used as the main knowledge sources. A preliminary evaluation was taken to show the usefulness of the system. RESULTS: Stability was shown on the MEDIS-based medical concept extraction with high precision. 73+/-14% (mean+/-S.D.) of the compound medical phrases extracted were sufficiently meaningful to form a medical concept from a clinical perspective. Also, 57.7% of attribute implication pairs (i.e. medical concept pairs) extracted were identified as positive from a clinical perspective. CONCLUSION: Under the framework of our ontology building support system using FCA, the clinical experts could reach a mass of both linguistic information and context-based knowledge that was demonstrated as useful to support their ontology building tasks.     

Biomedical Ontologies to Guide AI Development in Radiology

The advent of deep learning has engendered renewed and rapidly growing interest in artificial intelligence (AI) in radiology to analyze images, manipulate textual reports, and plan interventions. Applications of deep learning and other AI approaches must be guided by sound medical knowledge to assure that they are developed successfully and that they address important problems in biomedical research or patient care. To date, AI has been applied to a limited number of real-world radiology applications. As AI systems become more pervasive and are applied more broadly, they will benefit from medical knowledge on a larger scale, such as that available through computer-based approaches. A key approach to represent computer-based knowledge in a particular domain is an ontology. As defined in informatics, an ontology defines a domain’s terms through their relationships with other terms in the ontology. Those relationships, then, define the terms’ semantics, or “meaning.” Biomedical ontologies commonly define the relationships between terms and more general terms, and can express causal, part-whole, and anatomic relationships. Ontologies express knowledge in a form that is both human-readable and machine-computable. Some ontologies, such as RSNA’s RadLex radiology lexicon, have been applied to applications in clinical practice and research, and may be familiar to many radiologists. This article describes how ontologies can support research and guide emerging applications of AI in radiology, including natural language processing, image–based machine learning, radiomics, and planning.

     

Ontology development for unified traditional Chinese medical language system

Traditional Chinese medicine (TCM) as a complete knowledge system researches into human health conditions via a different approach compared to orthodox medicine. We are developing a unified traditional Chinese medical language system (UTCMLS) through an ontology approach that will support TCM language knowledge storage, concept-based information retrieval and information integration. UTCMLS is a huge knowledge project, which is a broad collaboration of 16 distributed groups, most of them with no prior experience of formal ontology development. Therefore, the cooperative and comprehensive ontology engineering is crucial. We use Protégé 2000 for ontology development of concepts and relationships that represent the domain and that will permit storage of TCM knowledge. This paper focuses on the methodology, design and development of ontology for UTCMLS.     

Co-occurrence graphs for word sense disambiguation in the biomedical domain

Word sense disambiguation is a key step for many natural language processing tasks (e.g. summarization, text classification, relation extraction) and presents a challenge to any system that aims to process documents from the biomedical domain. In this paper, we present a new graph-based unsupervised technique to address this problem. The knowledge base used in this work is a graph built with co-occurrence information from medical concepts found in scientific abstracts, and hence adapted to the specific domain. Unlike other unsupervised approaches based on static graphs such as UMLS, in this work the knowledge base takes the context of the ambiguous terms into account. Abstracts downloaded from PubMed are used for building the graph and disambiguation is performed using the personalized PageRank algorithm. Evaluation is carried out over two test datasets widely explored in the literature. Different parameters of the system are also evaluated to test robustness and scalability. Results show that the system is able to outperform state-of-the-art knowledge-based systems, obtaining more than 10% of accuracy improvement in some cases, while only requiring minimal external resources.