Despite widespread adoption of electronic health records (EHRs), most hospitals are not ready to implement data science research in the clinical pipelines. Here, we develop MEDomics, a continuously learning infrastructure through which multimodal health data are systematically organized and data quality is assessed with the goal of applying artificial intelligence for individual prognosis. Using this framework, currently composed of thousands of individuals with cancer and millions of data points over a decade of data recording, we demonstrate prognostic utility of this framework in oncology. As proof of concept, we report an analysis using this infrastructure, which identified the Framingham risk score to be robustly associated with mortality among individuals with early-stage and advanced-stage cancer, a potentially actionable finding from a real-world cohort of individuals with cancer. Finally, we show how natural language processing (NLP) of medical notes could be used to continuously update estimates of prognosis as a given individual’s disease course unfolds.

尽管电子健康记录 (EHR) 得到广泛采用，但大多数医院还没有准备好在临床管道中实施数据科学研究。在这里，我们开发了 MEDomics，这是一种持续学习的基础设施，通过该基础设施系统地组织多模式健康数据并评估数据质量，目标是应用人工智能进行个体预后。使用这个框架，目前由数千名癌症患者和十年数据记录的数百万个数据点组成，我们证明了这个框架在肿瘤学中的预后效用。作为概念证明，我们报告了使用此基础设施的分析，该分析确定弗雷明汉风险评分与早期和晚期癌症患者的死亡率密切相关，来自真实世界的癌症患者队列的潜在可行发现。最后，我们展示了如何使用医学笔记的自然语言处理 (NLP) 来不断更新对特定个体疾病进程的预后估计。