Cambridge Mathematics of Information in Healthcare (CMIH)

在我们最新一期CMIH的工作中，我们建立了一个强大的研究人员池，并在从数学、统计学到工程学、医学物理学和临床医生的各个领域开展合作。我们的工作也证实了成像数据是一个非常重要的诊断生物标志物，但健康记录、记忆测试和基因组学形式的非成像数据也是宝贵的预测资源，如果以适当的方式结合起来，应该是未来基于人工智能的医疗保健的来源。遵循这一理念，新的CMIH将来自数学、统计学、计算机科学和医学的研究人员与临床医生和相关行业利益相关者聚集在一起，开发严格的临床实用算法，用于以集成的方式分析医疗保健数据，以进行个性化诊断和治疗，以及在人口水平上进行目标识别和验证。我们将关注三个医学流派：癌症、心血管疾病和痴呆症，它们仍然是英国死亡和残疾的三大原因。尽管应用数学和数理统计仍然被普遍视为独立的学科，但越来越多的人认识到，通过消除历史上的学科界限，综合方法是唯一的前进道路。在使用多模式数据流解决数据科学中的方法挑战时尤其如此，例如我们将在Hub进行的研究。这种全面的方法将支持Hub的目标，即将用于医疗决策的人工智能带给临床最终用户。

In our work in the current edition of the CMIH we have built up a strong pool of researchers and collaborations across the board from mathematics, statistics, to engineering, medical physics and clinicians. Our work has also confirmed that imaging data is a very important diagnostic biomarker, but also that non-imaging data in the form of health records, memory tests and genomics are precious predictive resources and that when combined in appropriate ways should be the source for AI-based healthcare of the future.Following this philosophy, the new CMIH brings together researchers from mathematics, statistics, computer science and medicine, with clinicians and relevant industrial stakeholder to develop rigorous and clinically practical algorithms for analysing healthcare data in an integrated fashion for personalised diagnosis and treatment, as well as target identification and validation on a population level. We will focus on three medical streams: Cancer, Cardiovascular disease and Dementia, which remain the top 3 causes of death and disability in the UK.Whilst applied mathematics and mathematical statistics are still commonly regarded as separate disciplines there is an increasing understanding that a combined approach, by removing historic disciplinary boundaries, is the only way forward. This is especially the case when addressing methodological challenges in data science using multi-modal data streams, such as the research we will undertake at the Hub. This holistic approach will support the Hub aims to bring AI for healthcare decision making to the clinical end users.