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

机械建模与机器学习相结合诊断急性呼吸窘迫综合征

• 批准号: EP/Y003527/1
• 负责人: Sina Saffaran
• 金额: $ 17.54万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY003527%2F1
• 关键词: Combining; Mechanistic; Modelling; Machine; Learning

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.

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，以开发可以改善急性呼吸窘迫综合征（ARDS）的重症患者的诊断和治疗的系统。关键思想是使用机械性虚拟的患者在机械上进行机械医学培训，以使个人的医疗信息介绍给个人的医疗信息，并将其重新介绍为单个患者，并将其重新介绍为单个患者的信息。数据集，并允许改善仅受医疗状况驱动的患者群体的发现。正如《重症监护医学》杂志上的主要医学研究人员最近指出的那样，“人工智能方法（例如机器学习）可能有助于识别有ARDS的风险或满足ARDS诊断标准的患者，尽管该技术尚未为临床实施做好准备。降低医疗保健提供者的成本。