Semantic Web-Based Medical Documentation Processing for Healthcare Collaboration

用于医疗保健协作的基于语义网络的医疗文档处理

• 批准号: RGPIN-2014-03980
• 负责人: Benlamri, Rachid
• 金额: $ 1.46万
• 依托单位: Lakehead University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635010
• 关键词: Semantic; Web; Based; Medical; Documentation

The present landscape of healthcare is one of escalating costs coupled with an aging population with increasing complexity in concurrent chronic medical illnesses. Care provisioned to these patients are carried out by large interdisciplinary teams that are challenged with being synchronously aware of each member's view points and generating coherent and safe treatment plans. Part of challenges relating to this environment exists in how documentation occurs. The vast majority of documentation consists of unstructured/semi-structured text that occurs in silos. In order to fully understand the team's point of view, a team member would have to inspect every individual entry before being able to provide an integrated contribution to the treatment plan. Therefore, if one agrees that within medical documentation lies the very heart of clinical impressions and planning, what if a computer can (i) correctly parse and annotate medical documentation; (ii) build high semantic structures out of unstructured documents from various electronic sources, and (iii) predict clinical contexts. What actions could then a computer take that benefit the patient, the healthcare team, and all levels of decision makers? This research program addresses the above challenges by investigating a novel approach to medical documentation processing and analysis, whereby the document is no longer inert to information technology, but a rich source for knowledge acquisition and a road map to drive a wide range of software processes which range from collaborative treatment planning to just-in-time decision support. To accomplish this level of intelligence, we adopt a new approach to combining benefits of Model Driven Software Development (MDSD), ontology engineering, and context processing to solve (i), (ii) and (iii).

目前的医疗保健格局是成本不断上升，加上人口老龄化，并发慢性疾病的复杂性越来越高。为这些患者提供的护理是由大型跨学科团队进行的，他们面临着同步了解每个成员的观点并生成连贯和安全的治疗计划的挑战。与这种环境相关的部分挑战在于文档的生成方式。绝大多数文档由竖井中出现的非结构化/半结构化文本组成。为了充分理解团队的观点，团队成员必须检查每个单独的条目，然后才能对治疗计划做出综合贡献。因此，如果人们同意医学文档是临床印象和规划的核心，那么如果计算机可以(I)正确地解析和注释医学文档；(Ii)从来自各种电子来源的非结构化文档中构建高度语义结构，以及(Iii)预测临床上下文，会发生什么？那么，计算机可以采取哪些行动来使患者、医疗团队和所有级别的决策者受益？这项研究计划通过研究一种新的医疗文档处理和分析方法来解决上述挑战，通过这种方法，文档不再是信息技术的惰性文件，而是一个丰富的知识获取来源和路线图，以驱动从协作治疗计划到即时决策支持的广泛软件流程。为了实现这一智能级别，我们采用了一种新的方法来结合模型驱动软件开发(MDSD)、本体工程和上下文处理的优点来解决(I)、(II)和(III)。