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Machine learning and data informatics approaches for Personalised Outcome Prediction in Paediatric Intensive Care

儿科重症监护中个性化结果预测的机器学习和数据信息学方法

基本信息

批准号：
2589325
负责人：
金额：
--
依托单位：
University of Edinburgh
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2589325
关键词：
Machine learning data informatics approaches

项目摘要

This PhD project will develop machine learning and informatics algorithms for data-driven linkage of clinical data in paediatric critical care settings. We hypothesise that clinical, physiological and radiological (structural) data in paediatric patients with life-threatening brain trauma will inform about damage to the brain's ability to auto-regulate, and that combining and mining these multimodal data will enable the detection of patients - previously unidentified - at a higher risk of poor clinical outcomes.Routine clinical practice generates a large amount of data that is under-used for research and quality improvement. This is particularly true in paediatric intensive care units (PICU). Yet once the patient is discharged, vital information from this physiological big data is discarded rather than being used to advance our understanding of how a patient's physiological phenotype may affect outcome. Lack of linkage to other data sources collected during routine clinical care (e.g., radiological images, outcome such as re-admission) prevents meaningful use of this physiology data to advance patient care and safety. We urgently need to utilise data science to integrate the data generated from different sources during routine patient care and develop precision medicine approaches for critical care to deliver continuously improved patient care and outcome.

这个博士项目将开发机器学习和信息学算法，用于儿科重症监护环境中临床数据的数据驱动链接。我们假设临床，生理和放射学患有危及生命的脑外伤的儿科患者的（结构）数据将告知大脑自动调节能力的损害，结合和挖掘这些多模态数据将能够检测出以前未识别的患者，这些患者具有较高的不良临床结局风险。常规临床实践产生大量数据，这些数据在用于研究和质量改进。在儿科重症监护病房（PICU）中尤其如此。然而，一旦患者出院，来自这些生理大数据的重要信息就会被丢弃，而不是用于促进我们对患者生理表型如何影响结果的理解。缺乏与常规临床护理期间收集的其他数据源的联系（例如，放射学图像、诸如再次入院的结果）阻止了对该生理学数据的有意义的使用以促进患者护理和安全。我们迫切需要利用数据科学来整合常规患者护理过程中不同来源产生的数据，并为重症护理开发精准医学方法，以持续改善患者护理和结果。