Deriving an actionable patient phenome from healthcare data

从医疗保健数据中得出可操作的患者表型

• 批准号: MR/S004149/1
• 负责人: Honghan Wu
• 金额: $ 40.16万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS004149%2F1
• 关键词: Deriving; actionable; patient; phenome; healthcare

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.

将例行收集的卫生数据转化为知识是“学习型卫生系统”的要求。自从加入伦敦南部的生物医学研究中心和伦敦国王学院的莫斯利医院以来，我的研究一直专注于开发‘CogStack和SemEHR’。这是一个综合健康信息学平台，旨在解锁非结构化健康记录，并协助临床决策和研究。该系统对NHS内的深层数据进行了大量的浮出水面，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，以支持诸如为基因组学英国的100,000基因组计划招募患者[1，2]和预测药物不良反应[3]的研究。然而，知识和行动的产生还有相当大的潜力，例如通过将机器学习应用于该平台的数据。例如，通过这些系统返回的数据需要与生物医学知识进行整合、验证和清理，并用准确的临床上下文进行丰富(以增强当前的句子级语言上下文)，并与患者的时间表保持一致，以得出全面的患者表现组。临床知识需要从临床本体论中形式化，并与相关的开放数据相结合，这将推动自动推理，将较低级别的特征(例如数字血压读数)提升到较高级别的临床变量(例如高血压)，以支持决策。全面的表现组模型的初步研究，SemEHR的医疗概况[2]，基于来自重症监护医学信息市场(MIMIC)的公开可访问数据进行评估，已经证明，更好的背景信息可以在做出临床结论时带来更好的准确性-例如，使用患者病史对心房颤动进行亚型划分，在我们证明此类表型数据属于识别临床敏感患者群的前10个关键特征时。对于临床环境中的“action”生成，我们已经通过使用CogStack的一些简单示例演示了警报的可行性。例如，在国王学院医院，我们检测到25名患者服用甲氨蝶呤治疗类风湿性关节炎，预防潜在的致命肾功能衰竭的病理结果异常。拟议的研究将设计一个语义电子健康记录工具包，能够从非结构化和结构化的电子健康记录中派生出一致和全面的患者现象组，并提供语义计算，以支持定制护理、试验招募和研究的决策。参考文献：1.吴华，等。SemEHR：从电子健康记录中的临床笔记中浮现语义数据，用于定制护理、试验招募和临床研究。柳叶刀。2017年；390：S97.2。吴华，等人。一个通用的语义搜索系统，用于显示定制护理、试验招募和临床研究的临床笔记中的语义数据。美国医学信息学协会杂志。2017；doi：https://doi.org/10.1101/235622.3.Bean DM，吴华，等。电子健康档案中未知药物不良反应的知识图谱预测与验证。《科学》杂志2017年；7：16416。

项目成果

A Systematic Review of Natural Language Processing Applied to Radiology Reports

放射学报告中自然语言处理应用的系统综述

• DOI: 10.48550/arxiv.2102.09553
• 发表时间: 2021
• 作者: Casey A

The reporting quality of natural language processing studies: systematic review of studies of radiology reports.

• DOI: 10.1186/s12880-021-00671-8
• 发表时间: 2021-10-02
• 期刊: BMC medical imaging
• 影响因子: 2.7
• 作者: Davidson EM;Poon MTC;Casey A;Grivas A;Duma D;Dong H;Suárez-Paniagua V;Grover C;Tobin R;Whalley H;Wu H;Alex B;Whiteley W
• 通讯作者: Whiteley W

A systematic review of natural language processing applied to radiology reports.

• DOI: 10.1186/s12911-021-01533-7
• 发表时间: 2021-06-03
• 期刊: BMC medical informatics and decision making
• 影响因子: 3.5
• 作者: Casey A;Davidson E;Poon M;Dong H;Duma D;Grivas A;Grover C;Suárez-Paniagua V;Tobin R;Whiteley W;Wu H;Alex B
• 通讯作者: Alex B

Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study.

COVID-19 国家早期预警评分 (NEWS2) 的评估和改进：一项多医院研究

• DOI: 10.1186/s12916-020-01893-3
• 发表时间: 2021-01-21
• 期刊: BMC medicine
• 影响因子: 9.3
• 作者: Carr E;Bendayan R;Bean D;Stammers M;Wang W;Zhang H;Searle T;Kraljevic Z;Shek A;Phan HTT;Muruet W;Gupta RK;Shinton AJ;Wyatt M;Shi T;Zhang X;Pickles A;Stahl D;Zakeri R;Noursadeghi M;O'Gallagher K;Rogers M;Folarin A;Karwath A;Wickstrøm KE;Köhn-Luque A;Slater L;Cardoso VR;Bourdeaux C;Holten AR;Ball S;McWilliams C;Roguski L;Borca F;Batchelor J;Amundsen EK;Wu X;Gkoutos GV;Sun J;Pinto A;Guthrie B;Breen C;Douiri A;Wu H;Curcin V;Teo JT;Shah AM;Dobson RJB
• 通讯作者: Dobson RJB

Automated clinical coding: what, why, and where we are?

• DOI: 10.1038/s41746-022-00705-7
• 发表时间: 2022-10-22
• 期刊: NPJ digital medicine
• 影响因子: 15.2