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

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.

肺癌是世界范围内的主要死亡原因，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的早期发现和针对患者的针对性治疗对提高患者存活率至关重要。随着医疗保健数据量的持续增长，可以通过识别生物标志物来改进肺癌的诊断和治疗，这些生物标志物可用于识别罹患肺癌风险增加的患者或需要不同类型治疗的患者。近年来，越来越多的机器学习方法被用来帮助识别此类风险因素。然而，像蛋白质组学和电子健康记录这样的数据是非常具有挑战性的，因为这些数据是高维的，噪声的，包含各种来源的数据偏差。此外，它需要对临床和生物学领域的深入理解来正确地解释结果。在这个项目中，我们的目标是开发一个ML管道来识别非小细胞肺癌中可信的医疗风险因素和生物标记物。鉴于数据的性质和领域内的复杂性，我们提出了一种健壮的、可解释的和以人为中心的方法。一方面，我们希望通过使所用的最大似然分析更健壮来减少噪声和已知数据偏差的影响。另一方面，我们希望通过增加任何特定分析的可解释性并为他们提供工具来纳入他们的领域知识，从而简化临床专家对结果的评估。我们相信，在该项目中开发的技术可以很容易地应用于其他疾病领域，并将加速个性化医学的发展。