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Seeing the forest through the trees: uncovering phenomic complexity through interactive network visualization

机译:穿过树林看森林:通过交互式网络可视化发现表象学的复杂性

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摘要

Our aim was to uncover unrecognized phenomic relationships using force-based network visualization methods, based on observed electronic medical record data.A primary phenotype was defined from actual patient profiles in the Multiparameter Intelligent Monitoring in Intensive Care II database. Network visualizations depicting primary relationships were compared to those incorporating secondary adjacencies. Interactivity was enabled through a phenotype visualization software concept: the Phenomics Advisor.Subendocardial infarction with cardiac arrest was demonstrated as a sample phenotype; there were 332 primarily adjacent diagnoses, with 5423 relationships. Primary network visualization suggested a treatment-related complication phenotype and several rare diagnoses; re-clustering by secondary relationships revealed an emergent cluster of smokers with the metabolic syndrome.Network visualization reveals phenotypic patterns that may have remained occult in pairwise correlation analysis. Visualization of complex data, potentially offered as point-of-care tools on mobile devices, may allow clinicians and researchers to quickly generate hypotheses and gain deeper understanding of patient subpopulations.
机译:我们的目的是使用基于力的网络可视化方法,基于观察到的电子病历数据,发现无法识别的表型关系。根据重症监护II中多参数智能监控的实际患者资料定义了主要表型。将描述主要关系的网络可视化与包含次要邻接的网络可视化进行了比较。通过表型可视化软件概念启用了交互性:Phenomics Advisor。将具有心脏骤停的心内膜下梗死作为样本表型进行了展示。共有332个主要相邻的诊断与5423个关系。初级网络可视化提示与治疗有关的并发症表型和几种罕见的诊断。通过次级关系的重新聚类揭示了出现代谢综合征的吸烟者群。网络可视化揭示了成对相关性分析中可能仍然隐匿的表型。复杂数据的可视化(可能作为移动设备上的即时医疗工具提供)可以使临床医生和研究人员快速生成假设并加深对患者亚群的了解。

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