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**电子健康记录 (EHR) 的引入和纸质记录的逐渐消失导致医疗保健数据激增，其中大部分以自由文本记录形式保存，现在可以在不同的医疗保健环境中共享然而，数据量的增加，以及慢性疾病和合并症发生率的增加，意味着临床医生很难在分配的短时间内综合这些信息，这导致了临床医生可能无法综合这些信息的风险。对整体健康状况有一个全面的了解**愿景**该项目的愿景是建立机器学习模型，该模型将 (1) 分析与患者相关的所有临床记录，(2) 预测不同临床终点的风险，例如心脏病发作或死亡 (3) 并将此信息作为评分或警报呈现给临床医生，临床医生可以使用此信息来定制咨询、识别高风险患者并针对特定的临床结果。**目标**该可行性研究将进行评估。开发机器学习模型并在临床环境中实施它们的技术可行性。临床和技术专家的合作伙伴关系还将考虑如何将此类技术商业化以及最合适的商业模式是什么。**焦点**SCI-Diabetes 是世界知名的 EHR，拥有苏格兰 99% 的糖尿病患者的全面记录。可行性研究将侧重于使用这些数据预测糖尿病患者的不同临床终点。**创新**除了手动点击每个注释（这是一个耗时的过程）之外，临床医生没有办法回顾患者的整个病史。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）。该提案不是从自由文本中提取信息，而是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，甚至检测新的疾病进展模式。**合作伙伴**主要申请人 Red Star 将开发 ML 模型，合作者将是 NHSGG&C （数据、疾病和机器学习模型开发方面的专业知识）、Tactuum（英国和美国决策支持工具方面的专业知识）以及苏格兰 DHI 的 Ann Wales 博士，他也是苏格兰政府知识和决策支持总监 程序。”