Deriving an actionable patient phenome from healthcare data

将例行收集的卫生数据转化为知识是“学习型卫生系统”的一项要求。自从加入南伦敦生物医学研究中心和莫兹利医院、伦敦国王学院以来，我的研究一直集中在开发“CogStack和SemEHR”。这是一个集成的健康信息平台，旨在解锁非结构化的健康记录，并协助临床决策和研究。该系统在NHS内部的深层数据方面做了很多工作，例如通过在语义注释的临床笔记上提供以患者为中心的搜索来支持研究，例如为Genomics England的100，000 Genomes项目招募患者[1，2]和预测药物不良反应[3]。然而，在产生知识和行动方面还有相当大的进一步潜力，例如通过对来自该平台的数据应用机器学习。例如，通过这些系统返回的数据需要用生物医学知识进行整合、验证和清理，用准确的临床背景进行丰富（以增强当前的病历级语言背景），并与患者时间轴对齐，以得出全面的患者表型组。临床知识需要从临床本体中形式化，并与相关的开放数据集成，这将驱动自动推理以提升较低级别的功能（例如数字血压读数）直至更高级别的临床变量综合表型组模型的试点研究，SemEHR的医疗概况[2]，根据重症监护医学信息市场（MIMIC）的公开数据进行评估，已经证明，更好的背景信息可以导致更好的准确性，使临床结论-例如，使用患者病史来对心房纤颤进行分型，其中我们证明了这样的表型组数据在识别临床上敏感的患者群的前10个关键特征内。对于临床环境中的“动作”生成，我们已经通过使用CogStack的一些简单示例证明了警报的可行性。例如，在国王学院医院，我们发现25名因类风湿性关节炎而服用甲氨蝶呤的患者的病理结果异常，拟议的研究将设计一个语义电子健康记录工具包，该工具包能够从非结构化和结构化的电子健康记录中获得一致和全面的患者表型，并提供语义计算以支持决策进行量身定制的护理、试验招募和研究。参考文献：1. Wu H等人SemEHR：在电子健康记录中显现来自临床笔记的语义数据，用于定制护理、试验招募和临床研究。柳叶刀。2017;390：S97.2。Wu H等人，一个通用语义搜索系统，从定制护理、试验招募和临床研究的临床笔记中获取语义数据。美国医学信息学协会杂志。2017; doi：https://doi.org/10.1101/235622.3. Bean DM，Wu H，et al.未知药物不良反应的知识图预测和电子健康记录中的验证。2017;7：16416.

Translating routinely collected health data into knowledge is a requirement of a "learning health system". Since joining the Biomedical Research Centre at the South London and Maudsley Hospital, Kings College London, my research has been focused on developing 'CogStack and SemEHR'. This is an integrated health informatics platform which aims to to unlock unstructured health records and assist in clinical decision making and research. The system does much to surface the deep data within the NHS, for example through providing a patient-centric search on semantically annotated clinical notes to support studies such as the recruitment of patients for Genomics England's 100,000 Genomes project [1,2] and predicting adverse drug reactions [3]. However, there is considerable further potential for the generation of knowledge and action, for example through the application of machine learning to the data from this platform. For instance, the data returned through these systems needs to be integrated, verified and cleaned with biomedical knowledge, enriched with an accurate clinical context (to enhance the current sentence-level language context) and aligned with the patient timeline to derive a comprehensive patient phenome. Clinical knowledge needs to be formalised from clinical ontologies and integrated with relevant open data, which will drive automated inferences to lift lower-level features (e.g. numeric blood pressure readings) up to higher-level clinical variables (e.g. hypertension) for supporting decision making.A pilot study of the comprehensive phenome model, SemEHR's medical profiles [2], evaluated on publicly accessible data from the Medical Information Mart for Intensive Care (MIMIC), has proven that better contextual information can lead to much better accuracy in making clinical conclusions - e.g. using patient medical history for subtyping atrial fibrillation where we demonstrated that such phenome data is within the top 10 key features in identifying clinically-sensible patient clusters. For 'action' generation in clinical settings, we have demonstrated the feasibility of alerts through a number of simple examples using CogStack. For example, at Kings College Hospital, we have detected abnormal pathology results for 25 patients being prescribed methotrexate for rheumatoid arthritis, preventing potentially fatal renal failure.The proposed research will devise a semantic electronic health record toolkit that is able to derive a consistent and comprehensive patient phenome from unstructured and structured electronic health records and provide semantic computation upon it to support decision making for tailored care, trial recruitment and research. References: 1. Wu H, et al. SemEHR: surfacing semantic data from clinical notes in electronic health records for tailored care, trial recruitment, and clinical research. Lancet. 2017;390: S97.2. Wu H, et al. A General-purpose Semantic Search System to Surface Semantic Data from Clinical Notes for Tailored Care, Trial Recruitment and Clinical Research. Journal of the American Medical Informatics Association. 2017; doi: https://doi.org/10.1101/235622.3. Bean DM, Wu H, et al. Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records. Sci Rep. 2017;7: 16416.