Developing novel time-varying phenotype representations for clinical systems

表示学习推动了机器学习中的一些最大进步；例如，通过卷积网络(CNN)学习的成像数据集的低维、定性有意义的表示。临床数据缺乏这样的自然表征，这是制约相关机器学习进展的一个因素。这在决策领域尤其有问题，因为对特定患者来说，什么是最好的存在很大的不确定性。大多数干预措施都有潜在的有害副作用，即使是完善的重症监护病房也与随机对照结果的改善有关。因此，重要的是要开发出能够很好地代表患者状态的表现形式或健康表型。我们的目标是开发健康表型，并率先使用它们来理解健康意味着什么，识别相似的患者，改善医疗保健提供，并量化可能的干预措施的影响。 为了开发健康表型，我们需要整合涵盖免费文本笔记、编码程序和诊断、血压等重要指标的时间序列以及心电图等高频信号的数据。对于任何给定的患者，不同的数据对于特定的任务可能很重要。虽然之前的工作针对的是特定的任务(例如，纵向结果或诊断代码的预测)，但我们计划转而专注于整合不同数据类型的表示法，包括编码账单数据(稀疏二进制张量)、生命体征和实验室(密集不规则采样的时间序列数据)、临床叙述(可变长度文本向量)和基因组数据(密集二进制矩阵)。学习的表型必须是可靠和健壮的，因为在临床实践中应用机器学习可能会产生许多潜在的意外后果。将这些数据纳入表型潜伏状态估计需要跨越时间尺度和粒度的映射。 这项拟议的研究将对我们理解健康意味着什么以及人类表型的差异产生强烈影响。随着在实际环境中使用机器学习系统的意图，表示学习在其他领域的重要性也增加了。我们对异质数据中有意义的表型发现的关注在许多其他应用领域也很强大，在这些领域，合作者希望证明机器学习方法中学习的表示的健壮性。

Representation learning has prompted some of the greatest advances seen in machine learning; for example, the lower dimensional, qualitatively meaningful representations of imaging datasets learned by convolutional networks (CNNs). Clinical data lacks such natural representations, which is a limiting factor to the progress of relevant machine learning. This is especially problematic in the realm of decision making, where there is much uncertainty about what is best for a particular patient. Most interventions have potentially harmful side-effects, and even well-established intensive care unit are associated with improved outcomes in randomized controlled. It is therefore critical to develop representations or health phenotypes that are robust representations of patient state. Our goal is to develop health phenotypes, and pioneer their use to understand what it means to be healthy, identify similar patients, improve healthcare delivery, and quantify the impact of possible interventions. In order to develop health phenotypes, we need to integrate data that span free text notes, coded procedures and diagnoses, timeseries of vitals like blood pressure, and high-frequency signals like ECG. For any given patient, different data may be important for a specific task. While prior work has targeted specific tasks (e.g., the prediction of longitudinal outcomes or diagnostic codes, we plan to focus instead on representations that integrate distinct data types, including coded billing data (sparse binary tensors), vitals and labs (dense irregularly sampled time series data), clinical narratives (variable length text vectors), and genomics data (dense binary matrices). Learned phenotypes must be reliable and robust as there are many potential unintended consequences that may result from the application machine learning in clinical practice. Incorporating these data into phenotypic latent state estimates requires mapping across time-scales and granularity. The proposed research will have a strong impact on our understanding of what it means to be healthy, and variance within the human phenotype. The importance of representation learning in other fields has increased along with the intent to use the machine learning systems in a practical setting. Our focus on meaningful phenotypes discovery in heterogeneous data is also powerful in many other application areas where justifications about the robustness of a learned representation in machine learning methods are expected by collaborators.