Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul, Republic of Korea.
Sci Rep. 2020 Jan 29;10(1):1414. doi: 10.1038/s41598-020-58088-2.
In light of recent developments in genomic technology and the rapid accumulation of genomic information, a major transition toward precision medicine is anticipated. However, the clinical applications of genomic information remain limited. This lag can be attributed to several complex factors, including the knowledge gap between medical experts and bioinformaticians, the distance between bioinformatics workflows and clinical practice, and the unique characteristics of genomic data, which can make interpretation difficult. Here we present a novel genomic data model that allows for more interactive support in clinical decision-making. Informational modelling was used as a basis to design a communication scheme between sophisticated bioinformatics predictions and the representative data relevant to a clinical decision. This study was conducted by a multidisciplinary working group who carried out clinico-genomic workflow analysis and attribute extraction, through Failure Mode and Effects Analysis (FMEA). Based on those results, a clinical genome data model (cGDM) was developed with 8 entities and 46 attributes. The cGDM integrates reliability-related factors that enable clinicians to access the reliability problem of each individual genetic test result as clinical evidence. The proposed cGDM provides a data-layer infrastructure supporting the intellectual interplay between medical experts and informed decision-making.
鉴于基因组技术的最新发展和基因组信息的快速积累，预计将向精准医疗方向发生重大转变。然而，基因组信息的临床应用仍然有限。这种滞后可以归因于几个复杂的因素，包括医学专家和生物信息学家之间的知识差距、生物信息学工作流程与临床实践之间的距离，以及基因组数据的独特特征，这些都使得解释变得困难。在这里，我们提出了一种新的基因组数据模型，该模型可以在临床决策中提供更多的交互支持。信息建模被用作设计复杂生物信息学预测与与临床决策相关的代表性数据之间的通信方案的基础。这项研究是由一个多学科工作组进行的，他们通过失效模式和影响分析 (FMEA) 进行临床基因组工作流程分析和属性提取。基于这些结果，开发了一个具有 8 个实体和 46 个属性的临床基因组数据模型 (cGDM)。cGDM 集成了与可靠性相关的因素，使临床医生能够将每个个体遗传测试结果的可靠性问题作为临床证据来访问。所提出的 cGDM 提供了一个数据层基础设施，支持医学专家之间的智力互动和明智决策。
