Applying Natural Language Processing to real-world patient data to optimise cancer care

将自然语言处理应用于现实世界的患者数据以优化癌症护理

• 批准号: 2897525
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2897525
• 关键词: Applying; Natural; Language; Processing; real

Nearly 400,000 people are diagnosed with cancer each year, causing more than 167,000 deaths. Incidence and mortality are strongly associated with socioeconomic factors, with around 19,000 extra cancer deaths attributed to deprivation. Decisions on how best to treat cancer patients are based on evidence which is usually generated through the use of clinical trials. However, only a small fraction of patients participate in these studies, and many patient groups such as the frail, those with multiple medical problems, and ethnic minorities, are under-represented. This means there are large sections of the population, particularly the deprived (who suffer disproportionately from cancer), where the available evidence might not apply, perpetuating health inequalities. Routine 'real-world' patient data, collected about every patient as part of their normal treatment, offers an opportunity to provide evidence where clinical trial data doesn't or will not exist. The vision of this project is to learn from every patient treated.Artificial Intelligence approaches using real-world data can be used to understand patterns in cancer diagnosis and treatment, and to provide prospective assessment of the impact of healthcare innovations, but need the data to be structured to enable its processing. Modern Electronic Healthcare Records (EHRs) can collect data in the required format. However, historical data and many current sources of patient information (e.g. out-patient letters, radiology reports) often exist only as free-text medical notes, and therefore needs to be coded, i.e. structured, first. In this project, we will develop and apply Natural Language Processing (NLP) technologies to recover structured data from medical notes. We will then use these data to validate and improve models to predict cancer patients' clinical outcomes, and to see if patients' experience of their cancer treatment agrees with clinical assessments of their outcome and might provide early warning of evolving treatment related toxicity in head and neck cancer. This project is an exciting collaboration between the Division of Cancer Sciences and the Department of Computer Science/Alan Turing Institute, and as such the project student will benefit from close proximity to the clinical teams at The Christie NHS Foundation Trust, the largest single site cancer centre in Europe, and data science expertise at the University.

每年将近40万人被诊断出患有癌症，造成167,000多人死亡。发病率和死亡率与社会经济因素密切相关，大约有19,000个额外的癌症死亡归因于剥夺。关于如何最好地治疗癌症患者的决定是基于通常通过临床试验产生的证据。但是，只有一小部分患者参与了这些研究，许多患者群体，脆弱的患者，有多个医疗问题的患者和少数民族的人数不足。这意味着有大量人口，尤其是被剥夺的人（因癌症而遭受不成比例的痛苦），在这些证据可能不适用，使健康不平等永存。作为正常治疗的每位患者收集的常规“现实世界”患者数据提供了一个机会，可以提供证据，而临床试验数据不存在或不存在。该项目的愿景是向每位接受治疗的患者学习。使用现实世界数据的人工智能方法可用于了解癌症诊断和治疗中的模式，并对医疗创新的影响进行预期评估，但需要构造数据以启用其处理。现代电子医疗保健记录（EHRS）可以以所需的格式收集数据。但是，历史数据和许多当前的患者信息来源（例如门诊信，放射学报告）通常仅作为自由文本医学注释，因此需要编码，即结构化。在这个项目中，我们将开发和应用自然语言处理（NLP）技术，以从医疗笔记中恢复结构化数据。然后，我们将使用这些数据来验证和改进模型，以预测癌症患者的临床结果，并查看患者对癌症治疗的经验是否符合其结局的临床评估，并可能会在头部和颈部癌中对不断发展的治疗相关的毒性提供预警。该项目是癌症科学部与计算机科学/艾伦·图灵研究所（Alan Turing Institute）之间的激动人心的合作，因此，该项目的学生将受益于欧洲最大的单个现场癌症中心克里斯蒂NHS基金会信托基金会（Christie NHS Foundation Trust）的临床团队，这是欧洲最大的单一站点癌症中心，以及该大学的数据科学专业知识。