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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=708264
• 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).

目前的医疗保健状况是成本不断上升，加上人口老龄化，并发慢性医学疾病的复杂性不断增加。为这些患者提供的护理是由大型跨学科团队进行的，他们面临的挑战是同步了解每个成员的观点，并制定连贯和安全的治疗计划。与这一环境有关的部分挑战存在于文件编制的方式中。绝大多数文档都是由孤立的非结构化/半结构化文本组成的。为了充分理解团队的观点，团队成员必须检查每个条目，然后才能为治疗计划提供综合贡献。因此，如果一个人同意，在医疗文档中存在的临床印象和规划的核心，如果一台计算机可以（i）正确地解析和注释医疗文档;（ii）建立高语义结构的非结构化文件从各种电子来源，（iii）预测临床背景。那么，计算机可以采取哪些行动，使患者、医疗团队和各级决策者受益？该研究计划通过调查一种新的医疗文档处理和分析方法来解决上述挑战，该文档不再对信息技术惰性，而是知识获取的丰富来源和驱动范围广泛的软件流程的路线图，从协作治疗计划到及时决策支持。为了实现这种智能水平，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好处来解决（i），（ii）和（iii）。