Development of a decision support tool to improve the diagnosis and classification of myocardial infarction using signal processing and statistical ma

项目名称：利用信号处理和统计机器学习开发决策支持工具以改善心肌梗死的诊断和分类项目摘要该项目的目的是开发一种新的临床决策支持工具，该工具将为急诊科急性胸痛患者的评估提供准确的心肌梗死诊断和分类的个体概率。我们将在HighSTEACS试验（n= 54，000，clinicaltrials.gov NCT：01852523）的疑似急性冠状动脉综合征连续患者中使用我们的监督学习方法作为训练集，以确定信息和非信息变量。将在来自HiSTORIC试验的患者中进行验证和测试（n= 34，000分为两个数据集，clinicaltrials.gov NCT 03005158），以确定机器学习算法训练过程中新的不可见数据的模型校准和泛化。在这两项试验中，诊断分类（1-5型心肌梗死）由一组医生使用已建立的裁定门户网站和网络界面进行。该项目将开发新的数据挖掘工具，以处理心电图，并融合来自其他临床测试的多模态信息，从而对MI类型进行分类。- 实际了解临床实践和数据分析界面上的问题，包括两端利基术语的语言障碍-发展时间序列分析，信号处理，和统计机器学习，以解决大规模的挑战性问题-编程技能：将算法概念转换为软件工具，并开发可供专家使用的界面

Project title: Development of a decision support tool to improve the diagnosis and classification of myocardial infarction using signal processing and statistical machine learningProject summaryThe aim of the project is to develop a new clinical decision support tool that will provide accurate individual probabilities of the diagnosis and classification of myocardial infarction for the evaluation of patients with acute chest pain in the Emergency Department. We will use our supervised learning approach in consecutive patients with suspected acute coronary syndrome from the HighSTEACS trial (n=54,000, clinicaltrials.gov NCT:01852523) as the training set to determine informative and non-informative variables. Validation and testing will be performed in patients from the HiSTORIC trial (n=34,000 split into two datasets, clinicaltrials.gov NCT03005158) to determine model calibration and generalization in new, unseen data during the training process of the machine learning algorithm. In both trials the diagnostic classification (type 1-5 myocardial infarction) was performed by a team of physicians using an established adjudication portal and web-interface. The project will develop new data mining tools to process the electrocardiograms and fuse multi-modal information from additional clinical tests to classify MI types.Training outcomes: - Practical understanding of the problems at the interface of clinical practice and data analytics, including the language barrier with niche terminology on both ends - Developing expertise in time-series analysis, signal processing, and statistical machine learning in order to tackle large-scale challenging problems- Programming skills: transforming algorithmic concepts to software tools, and developing interfaces which can be used by experts