Scalable Biomedical Pattern Recognition Via Deep Learning

描述（由申请人提供）：从电子病历（emr）和其他生物医学数据集中提取的模式可以为学习医疗保健系统提供有价值的反馈，但我们找到它们的能力受到某些手动步骤的限制。寻找模式的主要方法是使用监督学习，其中计算算法在模拟结果变量（或标签）的输入变量（或特征）中搜索模式。这通常需要专家指定学习任务，构建输入特征，并准备结果标签。这种工作流程已经为我们服务了几十年，但是对人类努力的依赖阻止了它的扩展，并且它错过了最具信息量的模式，这些模式几乎是没有人预料到的。它不太适合人口规模数据的新兴时代，在这个时代，我们可以设想大量的新事业，如监测所有新出现的疾病，检测所有意想不到的药物效果，或推断所有遗传变异的完整临床表型。

DESCRIPTION (provided by applicant): Patterns extracted from Electronic Medical Records (EMRs) and other biomedical datasets can provide valuable feedback to a learning healthcare system, but our ability to find them is limited by certain manual steps. The dominant approach to finding the patterns uses supervised learning, where a computational algorithm searches for patterns among input variables (or features) that model an outcome variable (or label). This usually requires an expert to specify the learning task, construct input features, and prepare the outcome labels. This workflow has served us well for decades, but the dependence on human effort prevents it from scaling and it misses the most informative patterns, which are almost by definition the ones that nobody anticipates. It is poorly suited to the emerging era of population-scale data, in which we can conceive of massive new undertakings such as surveiling for all emerging diseases, detecting all unanticipated medication effects, or inferring the complete clinical phenotype of all genetic variants. The approach of unsupervised feature learning overcomes these limitations by identifying meaningful patterns in massive, unlabeled datasets with little or no human involvement. While there is a large literature on feature creation, a new surge of interest in unsupervised methods is being driven by the recent development of deep learning, in which a compact hierarchy of expressive features is learned from large unlabeled datasets. In the domains of image and speech recognition, deep learning has produced features that meet or exceed (by as much as 70%) the previous state of the art on difficult standardized tasks. Unfortunately, the noisy, sparse, and irregular data typically found in an EMR is a poor substrate for deep learning. Our approach uses Gaussian process regression to convert such an irregular sequence of observations into a longitudinal probability density that is suitable for use with a deep architecture. With this approach, we can learn continuous unsupervised features that capture the longitudinal structure of sparse and irregular observations. In our preliminary results unsupervised features were as powerful (0.96 AUC) in an unanticipated classification task as gold-standard features engineered by an expert with full knowledge of the domain, the classification task, and the class labels. In this project we will learn unsupervised features for records of all individuals in our deidentifed EMR image, for each of 100 laboratory tests and 200 medications of relevance to type 1 or type 2 diabetes. We will evaluate the features using three pattern recognition tasks that were unknown to the feature-learning algorithm: 1) an easy supervised classification task of distinguishing diabetics vs. nondiabetics, 2) a much more difficult task of distinguishing type 1 vs. type 2 diabetics, and 3) a genetic association task that considers the features as micro-phenotypes and measures their association with 29 different single nucleotide polymorphisms with known associations to type 1 or type 2 diabetes.