ICF: Using Explainable Artificial Intelligence to predict future stroke using routine historical investigations

ICF：使用可解释的人工智能通过常规历史调查来预测未来中风

• 批准号: MR/Y503472/1
• 负责人: Stephen Mullin
• 金额: $ 31.71万
• 依托单位: Plymouth University
• 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=MR%2FY503472%2F1
• 关键词: ICF; Using; Explainable; Artificial; Intelligence

There are more than 100,000 strokes in the UK each year causing 38,000 deaths, making it a leading cause of death and disability (NICE 2019). Ninety five percent of those who had a stroke had at least one untreated risk factor for it and 13.7% of these strokes were preventable. Findings that are predictive of future stroke are often identifiable on brain scans, electrocardiograms (ECG), heart scans (Echocardiogram or 'echo') and laboratory tests undertaken taken to investigate other medical problems. Often these signs are not picked up, which means evidence based treatments to reduce the risk of future stroke are not given. This is because of a lack of a dedicated system to do so. The first five years of care post stroke cost £3.60 billion (mean per patient cost: £46,039). Hence as well as improving many lives, a cost effective and accurate system to identify those at high risk of future stroke them will deliver major cost savings.Using 10,000 stroke cases seen at University Hospitals Plymouth NHS Trust (UHPNT) and with the close support of the Sentinel Stroke National Audit Programme (SSNAP - a national audit where all UK stroke cases are recorded), we hope to build a database of laboratory results, Magnetic resonance imaging (MRI) and computer tomography (CT) brain scans, ambulatory ECGs (ECGs which are worn for 24hr+) and echocardiograms, collected in those who later developed and did not develop a stroke. We shall use this data to train an artificial intelligence computer programme (model) which can predict who will later develop strokes based on patterns within the data collected. We believe that this approach will not only identify known risk factors for stroke, but may identify new patterns/features in one or across a number of investigations to predict future stroke. We hope this model will be the first step to building an automated system (which interfaces directly to GPs) for determining stroke risk and implementing treatments and lifestyle modifications to reduce this risk.

每年英国有100,000多人中风，造成38,000人死亡，这使其成为死亡和残疾的主要原因（NICE 2019）。中风的人中有95％至少有一个未经治疗的危险因素，而这些中风中有13.7％是可以预防的。预测未来中风的发现通常可以在脑部扫描，心电图（ECG），心脏扫描（超声心动图或“回声”）和实验室测试中进行确定。通常不会捡起这些迹象，这意味着没有给出基于证据的治疗方法来减少未来中风的风险。这是因为缺乏专门的系统。前五年的护理后中风耗资3600亿英镑（平均每个患者费用：46,039英镑）。因此，除了改善许多生命之外（MRI）和计算机断层扫描（CT）脑部扫描，ABSURATORATOR ECGS（ECG（佩戴的24小时+）和超声心动图，后来又收集了后来发展并没有中风的人。我们将使用此数据来培训人工智能计算机程序（模型），该计划可以预测谁将根据收集的数据中的模式进行中风。我们认为，这种方法不仅可以识别中风的已知风险因素，而且可以在许多调查中或在许多调查中确定新的模式/特征来预测未来的中风。我们希望该模型将成为构建自动化系统（直接与GPS接口）来确定中风风险并实施治疗和生活方式修改以降低这种风险的第一步。