基础设施工具,以支持一个有效的辐射瘤学学习卫生系统
Rishabh Kapoor1, William C Sleeman1, Preetam Ghosh1
1Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, USA.
Journal of applied clinical medical physics
|August 25, 2023
概括
本研究介绍了辐射瘤学学习健康系统 (RO-LHS) 的工具,以整合各种数据以改善患者护理. 这些工具促进了数据的发现和分析,使得持续学习和更好的临床决策在放射性瘤学.
科学领域:
- 医疗信息学 医疗信息学
- 辐射瘤学 辐射瘤学
- 数据科学数据科学数据科学
背景情况:
- 临床,剂量测量和治疗数据的整合对于推进放射性瘤治疗至关重要.
- 放射性瘤学学习健康系统 (RO-LHS) 为实时数据利用提供了一个框架,以提高护理质量.
- 数据管理中的现有挑战阻碍了RO-LHS实施的全部潜力.
研究的目的:
- 描述一套新的工具,旨在支持辐射瘤学习健康系统 (RO-LHS) 的发展.
- 解决当前在辐射瘤学中的数据集成,发现和分析方面的挑战.
- 促进实时使用集成的临床,剂量测量和研究数据.
主要方法:
- 使用基于知识图的方法将放射治疗数据映射到基于本体学的存储库中,遵守FAIR原则.
- 开发了一个本体学驱动的搜索引擎,用于高效的数据检索,包括同义词匹配和生物门户集成.
- 通过使用文本库创建和各种矢量嵌入模型 (Word2Vec,Doc2Vec,GloVe,FastText) 分析患者相似性,并使用共弦相似性和距离指标.
主要成果:
- 数据管道和工具使用1660个患者记录进行了验证,产生了超过50万个RDF组.
- 基于图形的可视化被用来有效地表示复杂的数据关系.
- 患者相似性分析表明,Word2Vec实现了最高的平均等号相似性,而GloVe提供了更紧的嵌入.
结论:
- 开发的框架和工具对建立一个功能性的RO-LHS具有重要作用.
- 这些工具提高了数据的可发现性和分析性,促进了持续的学习和改善放射性瘤患者护理.
- 该系统支持队列识别,临床决策支持,以及在辐射瘤学中推进机器学习计划.
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