Exploring data reduction strategies in the analysis of continuous pressure imaging technology.
Exploring data reduction strategies in the analysis of continuous pressure imaging technology.
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DOI：
10.1186/s12874-023-01875-y
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发表时间：
2023-03-01
影响因子：
4
通讯作者：
中科院分区：
文献类型：
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随着数字创新为持续的数据生成和存储带来新的能力，科学正变得越来越数据密集型。这一进展也带来了挑战，因为产生的剪切量数据对许多科学倡议构成了挑战。在这里，我们提出了一个案例研究的数据密集型随机临床试验，评估持续压力成像(CPI)的效用，以减少压力损伤。探索一种方法，使用嵌套的压力数据子集将分析所需的CPI数据量减少到可管理的大小，而不会丢失关键信息。被排除在研究分析阶段之外的四名登记研究参与者的数据被用来制定一种数据减少的方法。使用了两步数据策略。首先，在不同的频率(S的5、30、60、120和240)下对原始数据进行采样，以确定最佳测量频率。其次，使用相关系数来评估相邻帧之间的相似性，以识别登记的研究参与者的位置变化。通过使用热图和时间序列图的直观检查来评估数据策略的性能。S每隔60分钟的采样频率提供了界面压力随时间变化的合理表示。这种方法转化为在分析中仅使用所收集数据的1.7%。在第二步中，研究发现，24小时内的160帧代表了研究参与者的压力状态。总体而言，在72小时收集的数据中，只需要480帧就可以进行分析，而不会丢失信息。初步试验结果的评估只需要收集原始数据的0.2%的 ~ 。数据缩减是大数据分析的重要组成部分。我们的两步策略显著减少了分析所需的数据量，而不会丢失信息。这种数据简化策略如果得到验证，可以用于其他CPI和其他必须分析大量时间和空间数据的环境中。
Science is becoming increasingly data intensive as digital innovations bring new capacity for continuous data generation and storage. This progress also brings challenges, as many scientific initiatives are challenged by the shear volumes of data produced. Here we present a case study of a data intensive randomized clinical trial assessing the utility of continuous pressure imaging (CPI) for reducing pressure injuries. To explore an approach to reducing the amount of CPI data required for analyses to a manageable size without loss of critical information using a nested subset of pressure data. Data from four enrolled study participants excluded from the analytical phase of the study were used to develop an approach to data reduction. A two-step data strategy was used. First, raw data were sampled at different frequencies (5, 30, 60, 120, and 240 s) to identify optimal measurement frequency. Second, similarity between adjacent frames was evaluated using correlation coefficients to identify position changes of enrolled study participants. Data strategy performance was evaluated through visual inspection using heat maps and time series plots. A sampling frequency of every 60 s provided reasonable representation of changes in interface pressure over time. This approach translated to using only 1.7% of the collected data in analyses. In the second step it was found that 160 frames within 24 h represented the pressure states of study participants. In total, only 480 frames from the 72 h of collected data would be needed for analyses without loss of information. Only ~ 0.2% of the raw data collected would be required for assessment of the primary trial outcome. Data reduction is an important component of big data analytics. Our two-step strategy markedly reduced the amount of data required for analyses without loss of information. This data reduction strategy, if validated, could be used in other CPI and other settings where large amounts of both temporal and spatial data must be analysed.
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影响因子：
14.9
作者：
Baugh, L. R.;Hill, A. A.;Hunter, Craig P.
通讯作者：
Hunter, Craig P.
影响因子：
9.7
作者：
Bates, David W.;Saria, Suchi;Escobar, Gabriel
通讯作者：
Escobar, Gabriel
影响因子：
1.9
作者：
Motamedi, S. M.;de Grood, J.;Ghali, W. A.
通讯作者：
Ghali, W. A.
DOI：
10.1682/jrrd.2007.03.0046
发表时间：
2008-01-01
影响因子：
--
作者：
Bogie, Kath;Wang, Xiaofeng;Sun, Jiayang
通讯作者：
Sun, Jiayang
影响因子：
5.4
作者：
Luijf, Yoeri M.;Mader, Julia K.;DeVries, J. Hans
通讯作者：
DeVries, J. Hans