发现复杂疾病的亚表型可以帮助表征疾病队列,以进行旨在开发更好的诊断和治疗的调查研究。对电子健康记录(EHR)数据进行无监督机器学习的最新进展使研究人员能够在没有领域专家输入的情况下发现表型。然而,现有的大多数研究都忽略了时间,将疾病建模为离散事件.揭示表型的进化-它们是如何出现的,进化并有助于健康结果-对于定义更精确的表型和完善对疾病进展的理解至关重要。我们的目标是评估无监督方法的益处,该方法将时间模型疾病作为表型发现的动态过程。
在这项研究中,我们应用了约束非负张量因式分解方法,根据纵向EHR数据对心血管疾病(CVD)患者队列的复杂性进行了表征.通过张量分解,我们确定了一组表型主题(即,亚型)这些患者在诊断CVD之前的10年内建立的,并展示了进展模式。对于每个确定的亚表型,我们研究了其与美国心脏病学会/美国心脏协会集合队列风险方程估计的不良心血管结局风险的关联,临床实践中经常使用的常规CVD风险评估工具。此外,我们使用生存分析比较了6种最普遍的亚表型的心肌梗死(MI)发生率.
从12,380名成人心血管疾病个体的队列中,有1068个独特的PheCode,我们成功鉴定出14种亚型.通过与每个亚型估计的CVD风险的关联分析,我们发现了一些表型主题,如维生素D缺乏和抑郁症,尿路感染不能用常规的危险因素来解释。通过生存分析,我们发现,在六个最普遍的主题中,心血管疾病诊断后发生心肌梗死的风险明显不同(p<0.0001),表明这些主题可能捕获有临床意义的CVD亚表型.
这项研究证明了使用张量分解将疾病建模为来自纵向EHR数据的动态过程的潜在益处。我们的研究结果表明,这种数据驱动的方法可能有助于研究人员在精准医学研究中识别复杂和慢性疾病亚型。
Discovering subphenotypes of complex diseases can help characterize disease cohorts for investigative studies aimed at developing better diagnoses and treatments. Recent advances in unsupervised machine learning on electronic health record (EHR) data have enabled researchers to discover phenotypes without input from domain experts. However, most existing studies have ignored time and modeled diseases as discrete events. Uncovering the evolution of phenotypes - how they emerge, evolve and contribute to health outcomes - is essential to define more precise phenotypes and refine the understanding of disease progression. Our objective was to assess the benefits of an unsupervised approach that incorporates time to model diseases as dynamic processes in phenotype discovery.
In this study, we applied a constrained non-negative tensor-factorization approach to characterize the complexity of cardiovascular disease (CVD) patient cohort based on longitudinal EHR data. Through tensor-factorization, we identified a set of phenotypic topics (i.e., subphenotypes) that these patients established over the 10 years prior to the diagnosis of CVD, and showed the progress pattern. For each identified subphenotype, we examined its association with the risk for adverse cardiovascular outcomes estimated by the American College of Cardiology/American Heart Association Pooled Cohort Risk Equations, a conventional CVD-risk assessment tool frequently used in clinical practice. Furthermore, we compared the subsequent myocardial infarction (MI) rates among the six most prevalent subphenotypes using survival analysis.
From a cohort of 12,380 adult CVD individuals with 1068 unique PheCodes, we successfully identified 14 subphenotypes. Through the association analysis with estimated CVD risk for each subtype, we found some phenotypic topics such as Vitamin D deficiency and depression, Urinary infections cannot be explained by the conventional risk factors. Through a survival analysis, we found markedly different risks of subsequent MI following the diagnosis of CVD among the six most prevalent topics (p < 0.0001), indicating these topics may capture clinically meaningful subphenotypes of CVD.
This study demonstrates the potential benefits of using tensor-decomposition to model diseases as dynamic processes from longitudinal EHR data. Our results suggest that this data-driven approach may potentially help researchers identify complex and chronic disease subphenotypes in precision medicine research.