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/S004149/2
• 负责人: Honghan Wu
• 金额: $ 15.7万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS004149%2F2
• 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.

将常规收集的健康数据转化为知识是“学习卫生系统”的要求。自从加入伦敦国王学院南伦敦和莫德斯利医院的生物医学研究中心以来，我的研究一直致力于开发“ Cogstack and Semehr”。这是一个综合的健康信息学平台，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内的深度数据做得很大，例如，通过在语义注释的临床注释中提供以患者为中心的搜索来支持研究，例如英格兰基因组学的100,000个基因组项目[1,2]招募患者[1,2]并预测不良药物反应[3]。但是，例如，通过将机器学习应用于该平台的数据，具有很大的潜力来产生知识和行动。例如，通过这些系统返回的数据需要通过生物医学知识进行整合，验证和清洁，并具有准确的临床环境（以增强当前的句子级语言环境），并与患者时间表保持一致，以得出全面的患者现象。临床知识需要从临床本体论中进行形式化，并与相关的开放数据集成在一起，这将推动自动推论，以提高较低级别的特征（例如数值血压读数），直至高级临床变量（例如高血压）（例如，高血压），以支持综合现象模型的预测数据，对公共医疗服务的试验性数据，对公共医疗验证，SEMEHR的医疗信息，SEMEHR的MART [2] [2] [2] （模仿）证明，更好的上下文信息可以提高得出临床结论的更好准确性 - 例如使用患者病史来亚型房颤，我们证明了这种现象数据在识别临床上敏感的患者簇的前10位关键特征范围内。对于临床环境中的“动作”生成，我们通过使用Cogstack的许多简单示例证明了警报的可行性。例如，在国王学院医院，我们发现了25名被开具甲氨蝶呤的患者用于类风湿关节炎的患者的异常病理学结果，可防止潜在的致命肾衰竭。拟议的研究将为语义电子健康记录工具包设计，该工具能够设计出一种能够根据否定性和结构性的计算来制定一致和综合的患者，以启动和综合的电子记录，以估算和构造的计算，以估算和结构的计算，以估算和结构性计算，以估算和结构性的计算，以估算和结构性的计算，并构成效力，并构成了效果，并具有结构性的计算； 研究。参考文献：1。WuH等。 SEMEHR：从临床注释中浮出语义数据，用于量身定制的护理，试验招募和临床研究。柳叶刀。 2017; 390：S97.2。 Wu H等。通用语义搜索系统，从临床注释，量身定制的护理，试验招募和临床研究中表达语义数据。美国医学信息学协会杂志。 2017; doi：https：//doi.org/10.1101/235622.3。 Bean DM，Wu H等。知识图对电子健康记录中未知的不良药物反应和验证的预测。 SciRep。2017; 7：16416。

项目成果

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

ToKSA - Tokenized Key Sentence Annotation - a Novel Method for Rapid Approximation of Ground Truth for Natural Language Processing

• DOI: 10.1101/2021.10.06.21264629
• 发表时间: 2021-10
• 作者: C. Fairfield;W. Cambridge;L. Cullen;T. Drake;S. Knight;N. Masson;N. Mills;R. Pius;C. A. Shaw;H. Wu;S. Wigmore;A. Spiliopoulou;E. M. Harrison

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