Institut Curie, PSL Research University, CNRS, UMR168, 26 rue d'Ulm, 75005 Paris, France.
Sorbonne Université, 4, place Jussieu, 75005 Paris, France.
PLoS Comput Biol. 2020 May 18;16(5):e1007866. doi: 10.1371/journal.pcbi.1007866. eCollection 2020 May.
The precise diagnostics of complex diseases require to integrate a large amount of information from heterogeneous clinical and biomedical data, whose direct and indirect interdependences are notoriously difficult to assess. To this end, we propose an efficient computational approach to simultaneously compute and assess the significance of multivariate information between any combination of mixed-type (continuous/categorical) variables. The method is then used to uncover direct, indirect and possibly causal relationships between mixed-type data from medical records, by extending a recent machine learning method to reconstruct graphical models beyond simple categorical datasets. The method is shown to outperform existing tools on benchmark mixed-type datasets, before being applied to analyze the medical records of eldery patients with cognitive disorders from La Pitié-Salpêtrière Hospital, Paris. The resulting clinical network visually captures the global interdependences in these medical records and some facets of clinical diagnosis practice, without specific hypothesis nor prior knowledge on any clinically relevant information. In particular, it provides some physiological insights linking the consequence of cerebrovascular accidents to the atrophy of important brain structures associated to cognitive impairment.
复杂疾病的精确诊断需要整合来自异质临床和生物医学数据的大量信息，其直接和间接的相互依存关系众所周知难以评估。为此，我们提出了一种有效的计算方法，用于同时计算和评估任何混合类型（连续/分类）变量组合之间的多元信息的显著性。该方法通过将最近的机器学习方法扩展到超出简单分类数据集的图形模型重建，用于揭示来自医疗记录的混合类型数据之间的直接、间接和可能的因果关系。该方法在基准混合类型数据集上的表现优于现有工具，然后应用于分析来自巴黎 La Pitié-Salpêtrière 医院的认知障碍老年患者的医疗记录。所得到的临床网络直观地捕捉了这些医疗记录中的全局相互依存关系和临床诊断实践的某些方面，而没有关于任何临床相关信息的特定假设或先验知识。特别是，它提供了一些生理见解，将脑血管意外的后果与与认知障碍相关的重要大脑结构的萎缩联系起来。
