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本文引用的文献

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University of Queensland vital signs dataset: development of an accessible repository of anesthesia patient monitoring data for research.昆士兰大学生命体征数据集:开发一个可访问的麻醉患者监测数据存储库,用于研究。
Anesth Analg. 2012 Mar;114(3):584-9. doi: 10.1213/ANE.0b013e318241f7c0. Epub 2011 Dec 20.
2
Prediction of acute hypotensive episodes by means of neural network multi-models.神经网络多模型预测急性低血压发作。
Comput Biol Med. 2011 Oct;41(10):881-90. doi: 10.1016/j.compbiomed.2011.07.006. Epub 2011 Sep 6.
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Reconstruction of physiological signals using iterative retraining and accumulated averaging of neural network models.使用神经网络模型的迭代重训练和累积平均来重建生理信号。
Physiol Meas. 2011 Jun;32(6):661-75. doi: 10.1088/0967-3334/32/6/004. Epub 2011 May 12.
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Toward optimal display of physiologic status in critical care: I. Recreating bedside displays from archived physiologic data.朝向重症监护中生理状态的最优显示:I. 从存档的生理数据中重现床边显示。
J Crit Care. 2011 Feb;26(1):105.e1-9. doi: 10.1016/j.jcrc.2010.06.013. Epub 2010 Sep 1.
5
Standards for simulation in anaesthesia: creating confidence in the tools.麻醉模拟标准:建立对工具的信心。
Br J Anaesth. 2010 Jul;105(1):45-51. doi: 10.1093/bja/aeq095. Epub 2010 May 27.
6
Data sharing for pharmacokinetic studies.药代动力学研究中的数据共享。
Paediatr Anaesth. 2009 Oct;19(10):1005-10. doi: 10.1111/j.1460-9592.2009.03051.x. Epub 2009 Jun 25.
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Can anesthesia information management systems improve quality in the surgical suite?麻醉信息管理系统能否提高手术室的医疗质量?
Curr Opin Anaesthesiol. 2009 Apr;22(2):215-22. doi: 10.1097/ACO.0b013e328324b9e6.
8
The ANZICS CORE: an evolution in registry activities for intensive care in Australia and New Zealand.澳大利亚和新西兰重症监护学会核心项目:澳大利亚和新西兰重症监护登记活动的发展历程。
Crit Care Resusc. 2008 Jun;10(2):83-8.
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Electronic health records, medical research, and the Tower of Babel.电子健康记录、医学研究与巴别塔
N Engl J Med. 2008 Apr 17;358(16):1738-40. doi: 10.1056/NEJMsb0800209.
10
Predictors of postoperative acute renal failure after noncardiac surgery in patients with previously normal renal function.肾功能先前正常的患者非心脏手术后急性肾衰竭的预测因素。
Anesthesiology. 2007 Dec;107(6):892-902. doi: 10.1097/01.anes.0000290588.29668.38.

两份来自麻醉记录的开放获取、高质量数据集。

Two open access, high-quality datasets from anesthetic records.

机构信息

Centre for Medical and Health Science Education, University of Auckland, Auckland, New Zealand.

出版信息

J Am Med Inform Assoc. 2013 Jan 1;20(1):180-3. doi: 10.1136/amiajnl-2012-001087. Epub 2012 Aug 4.

DOI:10.1136/amiajnl-2012-001087
PMID:22865672
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3555334/
Abstract

OBJECTIVE

To provide a set of high-quality time-series physiologic and event data from anesthetic cases formatted in an easy-to-use structure.

MATERIALS AND METHODS

With ethics committee approval, data from surgical operations under general anesthesia were collected, including physiologic data, drug administrations, events, and clinicians' comments. These data were de-identified, formatted in a combined CSV/XML structure and made publicly available.

RESULTS

Two separate datasets were collected containing physiologic time-series data and time-stamped events for 34 patients. For 20 patients, the data included 400 physiologic signals collected over 20 h, 274 events, and 597 drug administrations. For 14 patients, the data included 23 physiologic signals collected over 69 h, with 286 time stamped comments.

DISCUSSION

Data reuse potentially saves significant time and financial costs. However, there are few high-quality repositories for accessible physiologic data and clinical interventions from surgical cases. De-identifying records assists with overcoming problems of privacy and storing the data in a format which is easily manipulated with computing resources facilitates access by the wider research community. It is hoped that additional high-quality data will be added. Future work includes developing tools to explore and visualize the data more efficiently, and establishing quality control measures.

CONCLUSION

An approach to collecting and storing high-quality datasets from surgical operations under anesthesia such that they can be easily accessed by others for use in research has been demonstrated.

摘要

目的

提供一套高质量的麻醉病例时间序列生理和事件数据,采用易于使用的结构。

材料和方法

获得伦理委员会批准后,收集全身麻醉手术中的生理数据、药物管理、事件和临床医生的评论。这些数据经过去标识处理,以 CSV/XML 格式组合并公开提供。

结果

收集了两个独立的数据集,包含 34 名患者的生理时间序列数据和时间戳事件。对于 20 名患者,数据包括 400 个生理信号,采集时间超过 20 小时,274 个事件和 597 次药物管理。对于 14 名患者,数据包括 23 个生理信号,采集时间超过 69 小时,有 286 个时间戳注释。

讨论

数据重用可以节省大量的时间和财务成本。然而,很少有可访问的来自手术病例的高质量生理数据和临床干预措施的存储库。记录去标识有助于解决隐私问题,并以易于使用计算资源处理的格式存储数据,便于更广泛的研究社区访问。希望能添加更多高质量的数据。未来的工作包括开发工具,以更有效地探索和可视化数据,并建立质量控制措施。

结论

已经证明了一种从麻醉手术中收集和存储高质量数据集的方法,以便其他人可以轻松访问这些数据集,用于研究。