Developing synergies between transient astronomy and early medical intervention

发展瞬变天文学和早期医疗干预之间的协同作用

• 批准号: ST/S002308/1
• 负责人: Robert Nichol
• 金额: $ 9.08万
• 依托单位: University of Portsmouth
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ST%2FS002308%2F1
• 关键词: Developing; synergies; between; transient; astronomy

The application of artificial intelligence (AI) to healthcare will revolutionise medicine and patient care over the coming decades. The wealth of data being collected by medical professionals keeps growing and they need significant help to quickly understand this information for their key diagnostic tests. In particular, can this wealth of digital data be used to automatically detect possible problems early, and thus alert doctors for closer inspection?Astronomy is also experiencing a data revolution driven by STFC experiments like LIGO and LSST. These experiments are opening up the time domain in astronomy and in the near-future astronomers will be deluged by millions of transient events a day; LSST will produce on average hundreds of transients per second! The prioritisation of so many events will be key to our success and astrophysicists are already developing and applying AI technology to help find the most interesting events within the ocean of "ordinary" transients. This project brings these two problems together by applying the knowledge and expertise developed in transient astronomy to the early automated diagnosis of medical problems. This project is at the forefront of interdisciplinary research bring world-leading cardiologists from King's College Hospital together with the best transient astrophysicists in the UK. Together, we hope to find early diagnostic signatures in detailed time-series patient data e.g. ECGs. This pilot activity will be used to prime-pump future investigations and grow the number of scientists engaged in such impactful research.

人工智能（AI）在医疗保健领域的应用将在未来几十年彻底改变医学和患者护理。医疗专业人员收集的大量数据在不断增长，他们需要重要的帮助来快速理解这些信息，以便进行关键的诊断测试。特别是，这些丰富的数字数据能否用于自动发现早期可能出现的问题，从而提醒医生进行更仔细的检查？天文学也正在经历一场由LIGO和LSST等STFC实验推动的数据革命。这些实验打开了天文学的时域，在不久的将来，天文学家每天将被数百万个瞬态事件淹没；LSST平均每秒会产生数百次瞬变！这么多事件的优先顺序将是我们成功的关键，天体物理学家已经在开发和应用人工智能技术，以帮助在“普通”瞬变的海洋中找到最有趣的事件。该项目通过将瞬态天文学的知识和专业知识应用于医学问题的早期自动诊断，将这两个问题结合在一起。这个项目是跨学科研究的前沿，汇集了国王学院医院世界领先的心脏病专家和英国最好的瞬态天体物理学家。总之，我们希望在详细的时间序列患者数据（如心电图）中找到早期诊断特征。这一试点活动将用于推动未来的调查，并增加从事这种有影响力的研究的科学家人数。

项目成果

Dark Energy Survey Year 1 Results: Cosmological constraints from cluster abundances and weak lensing

暗能量调查第一年结果：星团丰度和弱透镜效应的宇宙学限制

• DOI: 10.1103/physrevd.102.023509
• 发表时间: 2020
• 期刊: Physical Review D
• 影响因子: 5
• 作者: Abbott T

The Payload for Ultrahigh Energy Observations (PUEO): a white paper

• DOI: 10.1088/1748-0221/16/08/p08035
• 发表时间: 2020-10
• 期刊: Journal of Instrumentation
• 影响因子: 1.3
• 作者: Q. Abarr;P. Allison;J. Ammerman Yebra;J. Alvarez-Muñiz;J. Beatty;D. Besson;P. Chen;Y. Chen;C. Xie;J. Clem;A. Connolly;L. Cremonesi;C. Deaconu;J. Flaherty;D. Frikken;P. Gorham;C. Hast;C. Hornhuber;J. Huang;K. Hughes;A. Hynous;Y. Ku;C. Kuo;T. Liu;Z. Martin;C. Miki;J. Nam;R. Nichol;K. Nishimura;A. Novikov;A. Nozdrina;E. Oberla;S. Prohira;R. Prechelt;B. Rauch;J. M. Roberts;A. Romero-Wolf;J. W. Russell;D. Seckel;J. Shiao;D. Smith;D. Southall;G. Varner;A. Vieregg;S. Wang;Y. Wang;S. Wissel;R. Young;E. Zas;A. Zeolla

Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume II: DUNE Physics

深层地下中微子实验 (DUNE)，远探测器技术设计报告，第二卷：DUNE 物理

• DOI: 10.48550/arxiv.2002.03005
• 发表时间: 2020
• 期刊: arXiv e-prints
• 作者: Abi B.

Deep Underground Neutrino Experiment (DUNE) Near Detector Conceptual Design Report

深层地下中微子实验（DUNE）近探测器概念设计报告

• DOI: 10.3390/instruments5040031
• 发表时间: 2021
• 期刊: Instruments
• 作者: Manly, Steven;Kordosky, Mike;On behalf of the DUNE Collaboration, null
• 通讯作者: On behalf of the DUNE Collaboration, null

First results on ProtoDUNE-SP liquid argon time projection chamber performance from a beam test at the CERN Neutrino Platform

• DOI: 10.1088/1748-0221/15/12/p12004
• 发表时间: 2020-12-01
• 期刊: JOURNAL OF INSTRUMENTATION
• 影响因子: 1.3
• 作者: Abi, B.;Abud, A. Abed;Zwaska, R.
• 通讯作者: Zwaska, R.