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Machine Learning for Discovery of Patient Journey-Wide Phenotypes and Colorectal Cancer Stratification

用于发现患者整个旅程表型和结直肠癌分层的机器学习

基本信息

批准号：
2280988
负责人：
金额：
--
依托单位：
Queen's University Belfast
依托单位国家：
英国
项目类别：
Studentship
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=studentship-2280988
关键词：
Machine Learning Discovery Patient Journey

项目摘要

Colorectal cancer is the second highest cause of cancer mortality, associated with >880,000 deaths per annum worldwide. This project seeks to develop novel approaches for stratification of colorectal cancer patients in order to help inform clinical decision-making. For example, while a proportion of stage II colorectal cancer patients benefit from chemotherapy, it can be challenging to identify which specific patients will benefit [Kannarkatt et al. Journal of Oncology Practice 2017]. Cutting-edge informatics techniques will be applied to large datasets, including substantial linked clinical and demographic data, in order to discover fingerprints of individual characteristics that define new phenotypes across the patient journey. These data-driven patient phenotypes may include factors, for example relating to lifestyle, that influence the molecular processes driving cancer progression. Therefore discovery of patient phenotypes may define new cohorts for development of novel phenotype-specific molecular stratification approaches. Work during this four year studentship will be primarily based in the Overton group at Queen's University Belfast and associated with the Health Data Research UK Wales and Northern Ireland substantive site. The studentship includes six months to be spent at the LifeArc Centre for Diagnostics Development in Edinburgh, an ISO13485 certified environment. The student will benefit from LifeArc's considerable diagnostics development expertise, helping to ensure the anticipated novel diagnostic software is competent for potential clinical use.

结直肠癌是导致癌症死亡的第二大原因，全世界每年有8880000人死亡。该项目旨在开发结直肠癌患者分层的新方法，以帮助告知临床决策。例如，虽然一部分II期结直肠癌患者从化疗中受益，但确定哪些特定患者将受益可能具有挑战性[Kannarkatt等人]。Journal of Oncology Practice 2017]。尖端信息学技术将应用于大型数据集，包括大量相关的临床和人口统计数据，以发现个体特征的指纹，这些特征定义了整个患者旅程中的新表型。这些数据驱动的患者表型可能包括影响驱动癌症进展的分子过程的因素，例如与生活方式相关的因素。因此，患者表型的发现可能为开发新的表型特异性分子分层方法定义新的队列。在这个为期四年的学生期间，工作将主要基于贝尔法斯特女王大学的奥弗顿小组，并与英国健康数据研究威尔士和北爱尔兰实质性网站相关。该奖学金包括在爱丁堡的LifeArc诊断开发中心度过六个月，该中心通过了ISO13485认证。该学生将受益于LifeArc丰富的诊断开发专业知识，帮助确保预期的新型诊断软件能够胜任潜在的临床应用。