Multi-modal unsupervised embeddings to advance machine learning in healthcare

项目摘要 整合高维异构生物医学数据，如电子健康 记录（EHR），分子数据，成像和自由文本，是使强大的关键挑战 改变医疗保健的发现。目前文献中的工作通常分析 生物医学数据类型分开，侧重于小的疾病相关的患者队列，并依赖于 领域专家和以自组织方式进行手动临床特征选择。虽然 在某些情况下，特征空间的监督定义尺度很差， 很好地概括，包括固有的偏见，错过了发现新模式的机会， 功能.为了解决这些问题，我们将开发基于无监督的新方法。 机器学习以导出低维的基于向量的表示，即，“嵌入”， 从大规模、多模式和无领域的医学概念和患者临床历史 生物医学数据集这些预先计算的表示旨在克服常见的偏见， 人口、监督标签和具体的医院操作流程。这些多模式 嵌入可以被微调并应用于许多特定的预测任务， 提高机器学习模型的可扩展性、可推广性和有效性， 健康护理特别是，我们将首先开发基于无监督学习的方法， 使用异构EHR，链接生物库和 心电图波形数据，从不同人群的五个医院内的山 西奈卫生系统在纽约，纽约州，和公共可用的医学知识。然后我们将 创建一个可扩展的框架来计算无监督的多模态嵌入， 总结患者临床病史并导致亚型和患者分层。我们还将 开发一个联邦学习系统，以共享、可视化和联合收割机组合生成的嵌入 分别在不同的医疗机构，以捕捉更大和更多样化的人口， 临床景观我们将应用嵌入来推进基于EHR的疾病的方法 表型、发病预测和亚型。在EHR上进行测试时，遗传和波形数据 从链接的知识库和医学知识，所提出的方法将很容易 可扩展以包括其他数据，例如临床图像。这个项目将代表一个步骤 迈向医疗保健ML中的下一代ML，可以（i）扩展到数十亿患者，（ii） 嵌入多模态数据复杂关系，以及（iii）产生较少偏差的疾病 通过联邦学习安全地从各机构的患者那里学习。

PROJECT SUMMARY Integrating high-dimensional and heterogenous biomedical data, such as electronic health records (EHRs), molecular data, imaging, and free text, is a key challenge for making robust discoveries that transform healthcare. Current work in the literature commonly analyze biomedical data types separately, focus on small disease-related cohorts of patients, and rely on domain experts and manual clinical feature selection in an ad hoc manner. Although appropriate in some situations, supervised definitions of the feature space scale poorly, do not generalize well, include inherent biases, and miss opportunities to discover novel patterns and features. To address these issues, we will develop novel methods based on unsupervised machine learning to derive low-dimensional vector-based representations, i.e., “embeddings”, of medical concepts and patient clinical histories from large- scale, multi-modal and domain-free biomedical datasets. These pre-computed representations aim to overcome common biases due to population, supervised labeling, and specific hospital operation processes. These multi-modal embeddings can be fine-tuned and applied to a number of specific predictive tasks, improving scalability, generalizability and effectiveness of machine learning models in healthcare. In particular, we will first develop methods based on unsupervised learning to create multi-modal embeddings of medical concepts using heterogeneous EHRs, linked biobanks and electrocardiogram waveform data, from the diverse population of five hospitals within the Mount Sinai Health System in New York, NY, and publicly available medical knowledge. We will then create a scalable framework to compute unsupervised multi-modal embeddings that can summarize patient clinical histories and lead to subtyping and patient stratification. We will also develop a federated learning system to share, visualize, and combine embeddings generated separately at different medical institutes to capture a larger and more diverse population and clinical landscape. We will apply embeddings to advance methods for EHR-based disease phenotyping, onset prediction, and subtyping. While tested on EHRs, genetic and waveform data from linked repositories, and medical knowledge, the proposed approaches will be easily extendable to include other data, such as clinical images. This project will represent a step towards the next generation of ML in healthcare ML that can (i) scale to billions of patients, (ii) embed complex relationships of multi-modal data, and (iii) create less biased disease representations by securely learning from patients across institutions via federated learning.