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

该项目将联合收割机结合大规模电子患者数据、人工智能算法和机械数学模型，开发能够改善急性呼吸窘迫综合征（ARDS）危重患者诊断和治疗的系统。其核心思想是使用机械虚拟患者模型作为“过滤器”，提取个体患者的相关医疗信息，显著减少机器学习在异构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公司的Jörg Lippert博士建立合作关系，这将使我能够获得大规模的扩展患者数据和将机器学习应用于真实的临床问题的国际领先专业知识。正如领先的医学研究人员最近在《重症监护医学》杂志上所指出的那样，“人工智能方法（如机器学习）可能有助于识别处于ARDS风险或符合ARDS诊断标准的患者，尽管这项技术尚未准备好用于临床实施”。尽管医院成本巨大，但这项合作将在数字技术的临床应用方面取得突破，以更早地识别ARDS，改善患者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.