IEEE Trans Biomed Eng. 2021 Aug;68(8):2423-2434. doi: 10.1109/TBME.2020.3041815. Epub 2021 Jul 16.
Chronic diseases evolve slowly throughout a patient's lifetime creating heterogeneous progression patterns that make clinical outcomes remarkably varied across individual patients. A tool capable of identifying temporal phenotypes based on the patients different progression patterns and clinical outcomes would allow clinicians to better forecast disease progression by recognizing a group of similar past patients, and to better design treatment guidelines that are tailored to specific phenotypes. To build such a tool, we propose a deep learning approach, which we refer to as outcome-oriented deep temporal phenotyping (ODTP), to identify temporal phenotypes of disease progression considering what type of clinical outcomes will occur and when based on the longitudinal observations. More specifically, we model clinical outcomes throughout a patient's longitudinal observations via time-to-event (TTE) processes whose conditional intensity functions are estimated as non-linear functions using a recurrent neural network. Temporal phenotyping of disease progression is carried out by our novel loss function that is specifically designed to learn discrete latent representations that best characterize the underlying TTE processes. The key insight here is that learning such discrete representations groups progression patterns considering the similarity in expected clinical outcomes, and thus naturally provides outcome-oriented temporal phenotypes. We demonstrate the power of ODTP by applying it to a real-world heterogeneous cohort of 11 779 stage III breast cancer patients from the U.K. National Cancer Registration and Analysis Service. The experiments show that ODTP identifies temporal phenotypes that are strongly associated with the future clinical outcomes and achieves significant gain on the homogeneity and heterogeneity measures over existing methods. Furthermore, we are able to identify the key driving factors that lead to transitions between phenotypes which can be translated into actionable information to support better clinical decision-making.
慢性病在患者的一生中缓慢发展，形成异质的进展模式，导致个体患者的临床结局差异显著。一种能够根据患者不同的进展模式和临床结局识别时间表型的工具，可以让临床医生通过识别一组相似的既往患者来更好地预测疾病进展，并更好地设计针对特定表型的治疗指南。为了构建这样的工具，我们提出了一种深度学习方法，称为基于结果导向的深度时间表型分析（outcome-oriented deep temporal phenotyping，ODTP），以根据纵向观察结果识别可能出现的临床结局类型和时间来识别疾病进展的时间表型。更具体地说，我们通过时间到事件（time-to-event，TTE）过程来模拟患者纵向观察期间的临床结局，其条件强度函数使用递归神经网络估计为非线性函数。疾病进展的时间表型通过我们的新损失函数来进行，该函数专门设计用于学习离散的潜在表示，这些表示能够最好地描述潜在的 TTE 过程。这里的关键见解是，通过学习这些离散表示，根据预期临床结局的相似性来对进展模式进行分组，从而自然提供基于结果导向的时间表型。我们通过将其应用于来自英国国家癌症登记和分析服务的 11779 例 III 期乳腺癌真实异质队列，证明了 ODTP 的强大功能。实验表明，ODTP 识别的时间表型与未来的临床结局密切相关，并且在同质性和异质性测量方面显著优于现有方法。此外，我们能够确定导致表型之间转变的关键驱动因素，这些因素可以转化为可操作的信息，以支持更好的临床决策。
