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Motif discovery in hospital ward vital signs observation networks. 医院病房生命体征观测网络中的动机发现。
IF 2 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-01-01 Epub Date: 2024-10-07 DOI: 10.1007/s13721-024-00490-1
Rupert Ironside-Smith, Beryl Noë, Stuart M Allen, Shannon Costello, Liam D Turner

Vital signs observations are regular measurements used by healthcare staff to track a patient's overall health status on hospital wards. We look at the potential in re-purposing aggregated and anonymised hospital data sources surrounding vital signs recording to provide new insights into how care is managed and delivered on wards. In this paper, we conduct a retrospective longitudinal observational study of 770,720 individual vital signs recordings across 20 hospital wards in South Wales (UK) and present a network modelling framework to explore and extract behavioural patterns via analysis of the resulting network structures at a global and local level. Self-loop edges, dyad, triad, and tetrad subgraphs were extracted and evaluated against a null model to determine individual statistical significance, and then combined into ward-level feature vectors to provide the means for determining notable behaviours across wards. Modelling data as a static network, by aggregating all vital sign observation data points, resulted in high uniformity but with the loss of important information which was better captured when modelling the static-temporal network, highlighting time's crucial role as a network element. Wards mostly followed expected patterns, with chains or stand-alone supplementary observations by clinical staff. However, observation sequences that deviate from this are revealed in five identified motif subgraphs and 6 anti-motif subgraphs. External ward characteristics also showed minimal impact on the relative abundance of subgraphs, indicating a 'superfamily' phenomena that has been similarly seen in complex networks in other domains. Overall, the results show that network modelling effectively captured and exposed behaviours within vital signs observation data, and demonstrated uniformity across hospital wards in managing this practice.

生命体征观察是医护人员用来跟踪医院病房病人整体健康状态的常规测量方法。我们研究了围绕生命体征记录的汇总和匿名医院数据源的再利用潜力,以便为病房护理管理和服务提供新的见解。在本文中,我们对英国南威尔士 20 家医院病房的 770,720 次个人生命体征记录进行了回顾性纵向观察研究,并提出了一个网络建模框架,通过分析由此产生的全局和局部层面的网络结构来探索和提取行为模式。提取自环边缘、二元、三元和四元子图,并根据空模型进行评估,以确定单个统计意义,然后组合成病房级特征向量,为确定各病房的显著行为提供方法。通过汇总所有生命体征观测数据点,将数据建模为静态网络,虽然统一性很高,但却丢失了一些重要信息,而在建模静态-时间网络时,这些重要信息得到了更好的捕捉,突出了时间作为网络元素的关键作用。病房大多遵循预期模式,由临床人员进行连锁或独立的补充观察。然而,5 个已识别的主题子图和 6 个反主题子图揭示了偏离这一模式的观察序列。外部病房特征对子图相对丰度的影响也很小,这表明在其他领域的复杂网络中也存在类似的 "超家族 "现象。总之,研究结果表明,网络建模有效地捕捉和揭示了生命体征观察数据中的行为,并证明了各病房在管理这种行为方面的一致性。
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Network Modeling and Analysis in Health Informatics and Bioinformatics
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