A machine learning framework for trustworthy bio-medical risk factor identification – robust, explainable, and human-centred detection of endo- and phenotypes in lung cancer

用于识别值得信赖的生物医学风险因素的机器学习框架——对肺癌的内型和表型进行稳健、可解释且以人为本的检测

• 批准号: 10068410
• 金额: $ 6.37万
• 依托单位: OXFORD CANCER ANALYTICS LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10068410
• 关键词: machine; learning; framework; trustworthy; bio; machine; learning; framework; trustworthy; bio

Lung cancer is a leading cause of death worldwide, with non-small cell lung cancer (NSCLC) accounting for up to 85% of all cases and overall a 5-year survival rate of 17.8%. Both early detection and targeted patient-specific therapies of NSCLC are crucial to improve patient survival. As the amount of healthcare data is continuously growing, the diagnosis and treatment of lung cancer can be improved by identifying biomarkers which can be used to identify patients with an increased risk to develop the disease or which require a different type of therapy. Recently, an increasing number of machine learning approaches have been developed to facilitate the identification of such risk-factors.However, data like proteomics and electronic health records are very challenging to work with as the data is high dimensional, noisy, and contain various sources of data bias.Additionally, it requires a deep understanding of the clinical and biological domain to interpret results correctly.In this project we aim to develop a ML pipeline to identify trustworthy medical risk factors and biomarkers in NSCLC. Given the nature of the data and the complexity within the domain, we propose a robust, explainable, and human-centred approach. On the one side, we want to reduce the impact of noise and known data bias, by making the used ML analysis more robust. On the other side, we want to simplify the evaluation of results for clinical experts by increasing the explainability of any given analysis and by providing them with tools to include their domain knowledge.We believe that techniques developed within this project can easily be applied to other disease areas and will accelerate the development of personalised medicine.

肺癌是全球死亡的主要原因，非小细胞肺癌（NSCLC）占所有病例的85％，总体5年生存率为17.8％。 NSCLC的早期检测和针对性的患者特异性疗法对于改善患者生存至关重要。随着医疗保健数据的量不断增长，可以通过识别可用于鉴定出患有增加风险患者或需要不同类型治疗的患者的生物标志物来改善肺癌的诊断和治疗。最近，已经开发出越来越多的机器学习方法来促进这种风险因素的识别。但是，由于数据具有高维度，嘈杂性，并且包含各种数据源，并且在此方面，蛋白质组学和电子健康记录诸如蛋白质组学和电子健康记录非常具有挑战性。 NSCLC的危险因素和生物标志物。鉴于数据的性质和域内的复杂性，我们提出了一种健壮，可解释和以人为中心的方法。一方面，我们希望通过使使用的ML分析更强大，以减少噪声和已知数据偏差的影响。另一方面，我们希望通过提高任何给定分析的解释性并为它们提供包括其领域知识的工具来简化临床专家的结果评估。我们相信，在该项目中开发的技术可以轻松地应用于其他疾病领域，并会促进个性化医学的发展。