Combining Mechanistic Modelling with Machine Learning for Diagnosis of Acute Respiratory Distress Syndrome

该项目将结合大规模的电子患者数据、人工智能算法和机械数学模型，开发能够改善重症急性呼吸窘迫综合征(ARDS)患者诊断和治疗的系统。关键思想是使用机械虚拟患者模型作为“过滤器”来提取单个患者的相关医疗信息，显著减少机器学习在不同数据集上引入的偏差，我建议与亚琛大学的Andreas Schuppert教授和德国拜耳医疗保健的Jörg Lippert博士建立合作，使我能够访问大规模患者数据，并在将机器学习应用于实际临床问题方面获得国际领先的专业知识。正如领先的医学研究人员最近在《重症监护医学》杂志上指出的那样，“机器学习等人工智能方法可能有助于识别有ARDS风险的患者或满足ARDS的诊断标准，尽管这项技术还没有准备好临床实施。在ARDS中，患者结果很差，而医院成本高昂-这种合作将在数字技术的临床适用性方面取得突破，以便更早地识别ARDS，改善患者的治疗，并降低医疗保健提供者的成本。”

This project will combine large-scale electronic patient data, artificial intelligence algorithms, and mechanistic mathematical models, to develop systems that can improve the diagnosis, and hence treatment, of critically ill patients with acute respiratory distress syndrome (ARDS).The key idea is to use mechanistic virtual patient models as "filters" to extract relevant medical information on individual patients, significantly reducing biases introduced by machine learning on heterogeneous datasets, and allowing improved discovery of patient cohorts driven exclusively by medical conditions.I propose to establish a collaboration with Prof Andreas Schuppert at Aachen University and Dr Jörg Lippert at Bayer Healthcare in Germany that will give me access to large-scale patient data and internationally leading expertise in applying machine learning to real clinical problems. As noted recently by leading medical researchers in the journal Intensive Care Medicine, "Artificial Intelligence approaches such as machine learning may assist in identification of patients at risk of or fulfilling diagnostic criteria for ARDS, although this technology is not yet ready for clinical implementation".In ARDS, patient outcomes are poor, while hospital costs are huge - this collaboration will make breakthroughs in the clinical applicability of digital technologies for the earlier identification of ARDS, improving treatment of patients and reducing costs to healthcare providers.