Trust-MDx - Trustworthy Decision Limits for Multiplexed Diagnostics

我提出了一个全新的数学框架来利用超灵敏多重诊断测试的最新发展。早期和准确的疾病诊断对患者的治疗可能性、存活率和疫情预防有着巨大的影响。它允许医疗保健系统进行及时和相关的干预，并更有效地分配医疗资源。近年来，材料科学家们一直在努力提高测试灵敏度。这导致了利用各种纳米颗粒增强生物标志物信号的创新发展。最近使用的表面增强拉曼光谱提高了诊断测试的灵敏度，同时允许多重检测。不幸的是，材料科学家最近的巨大发展并没有伴随着数学建模的类似进步，这就是为什么支撑诊断测试的数学框架依赖于无效的百年建模技术。生物标志物检测中的常见问题涉及整合感兴趣的生物标志物特有的单个拉面带。这在灵敏度方面是非常昂贵的，这使得材料科学家最近的发展无效。通过基于潜在变量的模型，我将提高多重诊断测试的灵敏度。然而，这种建模方式将引入复杂的样本特定不确定性结构，必须对其进行精确估计，以促进基于多重诊断测试的有用决策。我利用机器学习原理，以高斯过程的形式来学习复杂的不确定性结构，从而缓解了这种情况，这将构成新的数学理论和实践，以促进基于多路诊断测试的决策。我将提高多重诊断测试的灵敏度和特异性，从而实现精确和早期的疾病诊断。

I propose a fundamentally I propose a fundamentally new mathematical framework to leverage recent developments in ultrasensitive multiplex diagnostic testing. Early and accurate disease diagnosis has a tremendous influence on patient treatment possibilities, survival rates, and outbreak prevention. It allows for timely and relevant interference by the healthcare system and more effective allocation of medical resources.In recent years there has been a large amount of effort directed at improving test sensitivity by material scientists. This led to innovative developments utilising various nanoparticles to enhance biomarker signals. The recent use of surface-enhanced Raman spectroscopy has improved the sensitivity of diagnostic tests while allowing for multiplex detection. Unfortunately, the enormous recent developments by material scientists have not been accompanied by similar advances in mathematical modelling, which is why the mathematical framework underpinning the diagnostic tests relies on ineffective century-old modelling techniques.Common problems in biomarker detection pertain to integrating a single Ramen band unique to the biomarker of interest. This is very costly in terms of sensitivity, which renders the recent developments by material scientists ineffective. Through latent variable-based models, I will increase the sensitivity of multiplex diagnostic tests. This way of modelling will, however, introduce complex sample-specific uncertainty structures, which must be precisely estimated to facilitate useful decision-making based on multiplex diagnostic tests. I mitigate this by utilizing machine-learning principles in a form of gaussian processes to learn the complex uncertainty structures.This will constitute new mathematical theories and practices to facilitate decision-making based on multiplexed diagnostic tests. I will improve the sensitivity and specificity of multiplexed diagnostic tests, and hence enable precise and early disease diagnosis.