Future blood testing for inclusive monitoring and personalised analytics Network+
Future blood testing for inclusive monitoring and personalised analytics Network+
批准号:
EP/W000652/1
负责人:
Weizi Li
金额:
$102.05万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2021
资助国家:
英国
项目状态:
未结题
起止时间:
2021 至 --
中文摘要
英国社区对基于实验室的血液检测的需求极高,分析表明,远程血液监测在未来将发挥重要作用,使患者和卫生专业人员能够远程进行自己的检测,极大地造福患者并加快决策速度。新冠肺炎疫情进一步突显了对远程和联网血液检测的需求,这超出了国民健康保险制度门诊设置的在线虚拟诊所的范围。在目前的社区卫生保健血液检测服务中,在没有培训和实验室设施的情况下,在临床环境之外获取和处理血液样本是具有挑战性的,患者被要求在旅行负担和感染风险的情况下前往全科医生手术或医院进行测试。许多血液分析是分批进行的,需要很长时间才能建立起来,这意味着常规测试的血样分析速度和诊断所需的时间是进一步的挑战。尽管最近在护理点方面进行了创新,但目前的血液分析工具在实践中主要是机械的或劳动密集型的,需要广泛的过滤和手动调整,不适合常规的家庭监测和纵向分析。随着时间的推移,没有个性化的实时方法来告知疾病的复杂性和情况,这对于及早发现急性疾病和管理慢性病至关重要。在英格兰,大约95%的临床路径依赖于患者能够获得高效、及时和具有成本效益的病理服务,每年进行5亿次生物化学和1.3亿次血液学测试。这意味着低效和不频繁的血液检测导致诊断延迟,对疾病进展和潜在并发症的了解不完整,涉及广泛人群。考虑到这些挑战和当前医疗保健领域的数字化转型,这是一个及时的机会,可以将研究人员、临床医生和工业家聚集在一起,应对血液监测和分析方面的挑战。拟议的Network+将建立一个跨学科社区,探索未来的血液检测解决方案,以实现远程、包容、快速、负担得起和个性化的血液监测,并解决社区卫生和护理方面的上述挑战。为了实现网络+愿景,将从信息和通信技术(ICT)、数据和分析科学、临床科学、应用光学、生物化学、工程和社会科学在网络+中的合作中进行技术研究。该网络将解决血液检测中的三个关键技术挑战:远程监测、ICT、个性化数据和一系列示范临床领域的人工智能,包括癌症、自身免疫性疾病、镰状细胞疾病、术前护理、病理服务和普通初级保健。
英文摘要
There is an extremely high demand for laboratory-based blood tests from community settings in the UK and analysis suggests an important role in the future for remote blood monitoring that would enable patients and health professionals to carry out their own tests remotely, greatly benefiting patients and speeding up decision making. The COVID-19 pandemic has further highlighted the need for remote and connected blood testing that is beyond the online virtual clinics in the NHS outpatient setting. In current blood testing services for community healthcare, it is challenging to obtain and process blood samples outside of the clinical setting without training and lab facilities, and patients are required to attend a GP surgery or hospital for tests with travel burden and infection risk. Many blood analyses are done in batches that take a long time to build up, meaning the speed of blood sample analysis of routine tests and time taken for diagnosis are further challenges. Despite recent innovations in point of care, current blood analysis tools in practice are mainly mechanical or labour-intensive that require extensive filtering and manual tweaking and not suitable for regular at-home monitoring and longitudinal analytics. There is no personalised real-time approach available to inform disease complexity and conditions over time, which are critical for early detection of acute diseases and the management of chronic conditions. In England, around 95% of clinical pathways rely on patients having access to efficient, timely and cost-effective pathology services and there are 500 million biochemistry and 130 million haematology tests are carried out per year. This means inefficient and infrequent blood testing leads to late diagnosis, incomplete knowledge of disease progression and potential complications in a wide range of populations. Taking those challenges into account and current digital transformation in healthcare, this is a timely opportunity to bring researchers, clinicians and industrialist together to address the challenges of blood monitoring and analytics.The proposed Network+ will build an interdisciplinary community that will explore future blood testing solutions to achieve remote, inclusive, rapid, affordable and personalised blood monitoring, and address the above challenges in community health and care. To achieve the Network+ vision, research of technologies will be conducted from collaborations among information and communication technology (ICT), data and analytical science, clinical science, applied optics, biochemistry, engineering and social sciences in the Network+. The network will address three key technical challenges in blood testing: Remote monitoring, ICT, Personalised data and AI in a range of examplar clinical areas including cancer, autoimmune diseases, sickle cell disease, preoperative care, pathology services and general primary care.
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P109 Early diagnosis of inflammatory arthritis (IA) using machine learning analysis of GP referral letters and blood tests to improve pre-hospital referral triage
P109 使用全科医生转诊信和血液检测的机器学习分析来早期诊断炎症性关节炎 (IA),以改善院前转诊分诊
DOI:
10.1093/rheumatology/keac133.108
发表时间:
2022
期刊:
Rheumatology
影响因子:
5.5
作者:
[Bradlow A]
通讯作者:
Bradlow A
DOI:
10.3389/fvets.2022.965622
发表时间:
2022
期刊:
Frontiers in veterinary science
影响因子:
3.2
作者:
[]
通讯作者:
DOI:
10.1109/tem.2023.3337000
发表时间:
2024
期刊:
IEEE Transactions on Engineering Management
影响因子:
5.8
作者:
[Ellen H. Hughes;Glenn Parry;Phil Davies;Veronica Martinez]
通讯作者:
Ellen H. Hughes;Glenn Parry;Phil Davies;Veronica Martinez
DOI:
10.1038/s41598-021-89189-1
发表时间:
2021-05-06
期刊:
Scientific reports
影响因子:
4.6
作者:
[Armstrong AJ, McCoy TM, Welbourn RJL, Barker R, Rawle JL, Cattoz B, Dowding PJ, Routh AF]
通讯作者:
Routh AF
DOI:
10.1016/j.snb.2021.131306
发表时间:
2022-01-06
期刊:
SENSORS AND ACTUATORS B-CHEMICAL
影响因子:
8.4
作者:
[Diaz-Fernandez, Ana, Bernalte, Elena, Di Lorenzo, Mirella]
通讯作者:
Di Lorenzo, Mirella
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批准号:2412340
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项目类别:Standard Grant
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财政年份:2023
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负责人:Weizi Li
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依托单位:
Advancing machine learning to achieve real-world early detection and personalised disease outcome prediction of inflammatory arthritis
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批准号:EP/Y019393/1
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项目类别:Research Grant
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资助金额:$78.96万
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财政年份:2023
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负责人:Weizi Li
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依托单位:
CRII: III: Towards Effective and Efficient City-scale Traffic Reconstruction
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批准号:2153426
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项目类别:Standard Grant
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资助金额:$17.48万
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财政年份:2022
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负责人:Weizi Li
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依托单位:
国内基金
海外基金
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