The co-occurrence of multiple long-term conditions (MLTC), or multimorbidity, in an individual can reduce their lifespan and severely impact their quality of life. Exploring the longitudinal patterns, e.g. clusters, of disease accrual can help better understand the genetic and environmental drivers of multimorbidity, and potentially identify individuals who may benefit from early targeted intervention. We introduce $\textit{probabilistic modelling of onset times}$, or $\texttt{ProMOTe}$, for clustering and forecasting MLTC trajectories. $\texttt{ProMOTe}$ seamlessly learns from incomplete and unreliable disease trajectories that is commonplace in Electronic Health Records but often ignored in existing longitudinal clustering methods. We analyse data from 150,000 individuals in the UK Biobank and identify 50 clusters showing patterns of disease accrual that have also been reported by some recent studies. We further discuss the forecasting capabilities of the model given the history of disease accrual.

翻译：个体中多重长期疾病（MLTC）或共病的共存可能缩短其寿命并严重影响其生活质量。探索疾病累积的纵向模式（例如聚类）有助于更好地理解共病的遗传和环境驱动因素，并可能识别出可能受益于早期靶向干预的个体。我们提出了发病时间的概率建模（ProMOTe），用于聚类和预测MLTC轨迹。ProMOTe能够无缝地从电子健康记录中常见但现有纵向聚类方法常忽略的不完整和不可靠疾病轨迹中学习。我们分析了英国生物银行中150,000名个体的数据，识别出50个显示疾病累积模式的聚类，这些模式也已被近期一些研究报道。我们进一步讨论了模型在给定疾病累积历史情况下的预测能力。