Predicting Clinical Events Using NLP Analysis of Clinical Notes in Diabetes Patients

使用 NLP 分析糖尿病患者的临床记录来预测临床事件

• 批准号: 133841
• 金额: $ 6.58万
• 依托单位: RED STAR CONSULTING LIMITED
• 依托单位国家: 英国
• 项目类别: Feasibility Studies
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=133841
• 关键词: Predicting; Clinical; Events; Using; NLP

"**Overview**The introduction of Electronic Health Records (EHR) and the move away from paper notes has led to a proliferation of healthcare data, much of it held in free-text notes, which can now be shared across different healthcare settings and clinical specialties. However the increase in volume of data, alongside increasing rates of chronic illness and co-morbidities, has meant that clinicians struggle to synthesise this information within the short appointment times allocated. This gives rise to the risk that the clinician may not have a full picture of the overall health of the patient and may miss important symptoms.**Vision**The vision of this project is to build Machine Learning models which will (1) analyse all the clinical notes associated with a patient, (2) predict the risk of different clinical endpoints such as heart attack or death (3) and present this information to the clinician as a score or alert. Clinicians can use this to tailor the consultation, identify high risk patients, and target specific clinical outcomes.**Objectives**This feasibility study will assess the technical feasibility of developing ML models and implementing them in a clinical setting. The collaborative partnership of clinical and technical expertise will also consider how to commercialise such technology and what is the most appropriate business model.**Focus**SCI-Diabetes is a world renowned EHR which has comprehensive records for 99% of diabetes patients in Scotland. The feasibility study will focus on predicting different clinical endpoints for diabetic patients using this data.**Innovation**Other than manually clicking into each note - a time consuming process - there is no way for clinicians to review the entire history of a patient. Most other NLP approaches aim to extract structured information from free text and convert these into clinical codes (such as identifying mentions of specific diseases).Instead of extracting information from free text, this proposal uses the text to directly predict different clinical endpoints. As well as analysing the entire patient history, the model will benefit from being able to aggregate different clinical judgements and even detect new patterns of disease progression.**Partners**The lead applicant Red Star will develop the ML models and collaborators will be NHSGG&C (expertise on the data, disease and development of ML models), Tactuum (expertise in decision support tools in both UK and USA) and Dr Ann Wales from DHI Scotland who is also Director for Scottish Government Knowledge and Decision Support Programme."

“ **概述**引入电子健康记录（EHR）以及远离纸质笔记的转移导致了医疗保健数据的泛滥，其中大部分在自由文本注释中持续存在，现在可以在不同的医疗保健环境和临床专业中共享这些数据，但是随着时间的增加，该数据的数量增加了，该临床的数量增加了，这是在慢性病和持续的率增加。分配的。确定高风险患者，并针对特定的临床结果。**目标**这项可行性研究将评估开发ML模型并在临床环境中实施的技术可行性。临床和技术专长的合作伙伴关系还将考虑如何商业化此类技术以及最合适的商业模式。** Focus ** Sci-Diabetes是世界著名的EHR，它具有99％的斯科特兰糖尿病患者的全面记录。可行性研究将着重于使用这些数据预测糖尿病患者的不同临床终点。大多数其他NLP方法旨在从自由文本中提取结构化信息并将其转换为临床代码（例如识别特定疾病的提及）。该提案使用文本直接预测不同的临床终点。除了分析整个患者病史外，该模型还将受益于能够汇总不同的临床判断，甚至检测疾病进展的新模式。苏格兰政府知识和决策支持计划。”