Center for Computational Science (CCS), Miller School of Medicine, University of Miami, Miami, FL 33136, USA; Laboratory of Integrative Systems Medicine (LISM), Institute of Clinical Physiology (IFC), National Research Council (CNR), Pisa 56124, Italy.
Trends Mol Med. 2013 Sep;19(9):515-21. doi: 10.1016/j.molmed.2013.07.004. Epub 2013 Aug 12.
Comorbidity represents an extremely complex domain of research. An individual entity, the patient, is the center of gravity of a system characterized by multiple, complex, and interrelated conditions, disorders, or diseases. Such complexity is influenced by uncertainty that is difficult to decipher and is proportional to the number of associated morbidities. Computational scientists usually provide meta-analysis studies aimed at integrating various types of evidence, but in our opinion they may help reformulate comorbidity by emphasizing, in particular, two aspects: (i) a systems approach, which allows for an ensemble view of comorbidity, and offers a model representation generalizable to multimorbidity; and (ii) a dynamic network inference approach, which is indicated for the analysis of links among morbidities and evaluation of risk. Notably, the main question remains whether such instruments suggest a shift of paradigm providing prospective impact on medical practice. We have identified in the simultaneous consideration of multiple dimensions linked to comorbidity complexity the rationale for such translation.
合并症是一个极其复杂的研究领域。个体患者是一个以多种复杂且相互关联的病症、障碍或疾病为特征的系统的重心。这种复杂性受到难以解读的不确定性的影响，且与相关合并症的数量成正比。计算科学家通常提供旨在整合各种类型证据的荟萃分析研究，但在我们看来，它们可能有助于通过特别强调以下两个方面来重新构建合并症：（i）系统方法，它允许对合并症进行整体观察，并提供可推广到多病共存的模型表示；以及（ii）动态网络推理方法，它适用于分析多种合并症之间的关联和评估风险。值得注意的是，主要问题仍然是这些工具是否预示着范式的转变，从而对医学实践产生前瞻性影响。我们已经确定，在同时考虑与合并症复杂性相关的多个维度时，这种转变是合理的。
