Machine Learning for Patient-Specific, Predictive Healthcare Technologies via Intelligent Electronic Health Records

通过智能电子健康记录实现针对特定患者的预测性医疗保健技术的机器学习

• 批准号: EP/N020774/1
• 负责人: David Clifton
• 金额: $ 128.66万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FN020774%2F1
• 关键词: Machine; Learning; Patient; Specific; Predictive

Healthcare systems world-wide are struggling to cope with the demands of ever-increasing populations in the 21st-century, where the effects of increased life expectancy and the demands of modern lifestyles have created an unsustainable social and financial burden. However, healthcare is also entering a new, exciting phase that promises the change required to meet these challenges: ever-increasing quantities of complex data concerning all aspects of healthcare are being stored, throughout the life of a patient. These include electronic health records (EHRs) now active in many hospitals, and large volumes of data being collected by patient-worn sensors. The resulting rapid growth in the amount of data that is stored far outpaces the capability of clinical experts to cope. There is huge potential for using advances in computer science to use these huge datasets. This promises to improve healthcare outcomes significantly by allowing the development of new technologies for healthcare using the data - this is an area that promises to develop into a major new field in medicine. Making sense of the complex data is one of the key challenges for exploiting these massive datasets. This programme aims to establish a new centre focussed on developing the next generation of predictive healthcare technologies, exploiting the EHR using new methods in computer science. We describe a number of healthcare themes which demonstrate the potential to improve patient outcomes. This will be achieved in collaboration with a consortium of leading clinicians and healthcare companies. The primary aim is to develop the "Intelligent EHR", which will have applications in creating "early warning systems" to predict patient problems (such as heart failure), and to help doctors know which drug or treatment would best be used for each individual patient - by interpreting the vast quantities of data available in the EHR.

世界各地的医疗保健系统都在努力应对21世纪不断增长的人口需求，预期寿命的延长和现代生活方式的需求造成了不可持续的社会和经济负担。然而，医疗保健也正在进入一个令人兴奋的新阶段，它承诺应对这些挑战所需的变化：在患者的整个生命周期中，存储的涉及医疗保健各个方面的复杂数据的数量不断增加。其中包括目前在许多医院中使用的电子健康记录（EHRs），以及由患者佩戴的传感器收集的大量数据。由此产生的存储数据量的快速增长远远超过了临床专家应对的能力。利用计算机科学的进步来利用这些庞大的数据集有着巨大的潜力。通过允许使用这些数据开发医疗保健新技术，这有望显著改善医疗保健结果——这是一个有望发展成为医学主要新领域的领域。理解复杂的数据是利用这些海量数据集的关键挑战之一。该计划旨在建立一个新的中心，重点开发下一代预测医疗技术，利用计算机科学的新方法开发电子病历。我们描述了一些医疗保健主题，展示了改善患者预后的潜力。这将与领先的临床医生和医疗保健公司组成的联盟合作实现。主要目标是开发“智能电子病历”，它将应用于创建“早期预警系统”来预测病人的问题（如心力衰竭），并帮助医生知道哪种药物或治疗方法最适合每个病人——通过解释电子病历中可用的大量数据。

项目成果

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review.

• DOI: 10.1109/rbme.2017.2763681
• 发表时间: 2018
• 期刊: IEEE reviews in biomedical engineering
• 影响因子: 17.6
• 作者: Charlton PH;Birrenkott DA;Bonnici T;Pimentel MAF;Johnson AEW;Alastruey J;Tarassenko L;Watkinson PJ;Beale R;Clifton DA
• 通讯作者: Clifton DA

Wearable remote monitoring for patients with COVID-19 in low-resource settings: case study

• DOI: 10.1136/bmjinnov-2021-000706
• 发表时间: 2021-03-01
• 期刊: BMJ INNOVATIONS
• 影响因子: 2
• 作者: Nguyen Van Vinh Chau;Ho Bich Hai;Thwaites, C. Louise
• 通讯作者: Thwaites, C. Louise

Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling

用于异质疾病进展建模的输入输出混合隐马尔可夫模型

• DOI: 10.1109/bhi56158.2022.9926903
• 发表时间: 2022
• 作者: Ceritli T

Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.

• DOI: 10.1371/journal.pone.0260476
• 发表时间: 2021
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Bishop JA;Javed HA;El-Bouri R;Zhu T;Taylor T;Peto T;Watkinson P;Eyre DW;Clifton DA
• 通讯作者: Clifton DA

Continuous Patient State Attention Models

连续患者状态注意力模型

• DOI: 10.1101/2022.12.23.22283908
• 发表时间: 2022
• 作者: Chauhan V