Section for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
Division of Nephrology and Dialysis, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria.
BMC Med Res Methodol. 2022 Mar 6;22(1):62. doi: 10.1186/s12874-022-01544-6.
Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space.
We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000-2020 in the electronic databases PubMed, Scopus and Embase.
Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual's contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction.
The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling.
生物技术的最新进展使人们能够获取个体的高维数据，这对预测模型提出了挑战，因为传统的预测模型通常使用临床患者特征等协变量。对于从这些现代数据模态中提取的特征，应该考虑采用其他形式的协变量表示方法，这些方法可以利用其内在的相互连接。这些特征之间的连接信息可以表示为一个由节点和边组成的个体特定网络，其强度可以因人而异。描述网络的全局或局部图论特征可以构成潜在的预后生物标志物，替代或补充传统的协变量，并可能取代在高维预测空间中寻找个体生物标志物的常用方法。
我们进行了范围界定综述，以确定、整理和批判性评价 2000 年至 2020 年间在医学和应用健康研究中使用个体特定网络进行预测建模的最新研究，使用的电子数据库包括 PubMed、Scopus 和 Embase。
我们的范围界定综述揭示了主要的应用领域，即神经病学和病理心理学，其次是癌症研究、心脏病学和病理学（N=148）。网络构建主要基于重复测量的 Pearson 相关系数，但也发现了其他方法（如部分相关、可见度图）。对于每个个体仅测量一次的协变量，网络构建主要基于量化个体对整体组级结构的贡献。尽管确定了个体特定网络推断的多种方法，但旨在预测未来个体临床结局的研究数量相当有限，而且大多数模型只是证明了网络特征在原则上可以用于预测的概念。
目前的研究结果清楚地表明，个体特定网络分析对于预测建模具有重要价值，但它尚未被视为当前应用领域之外的通用工具。需要对基于良好策略的网络推断进行更多的方法学研究，特别是在适当的网络稀疏化以及基于结果的图论特征提取和选择方面，以及如何有效地利用网络进行预测建模方面。
