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A Karnaugh map based approach towards systemic reviews and meta-analysis

机译：基于卡诺图的系统评价和荟萃分析方法

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Studying meta-analysis and systemic reviews since long had helped us conclude numerous parallel or conflicting studies. Existing studies are presented in tabulated forms which contain appropriate information for specific cases yet it is difficult to visualize. On meta-analysis of data, this can lead to absorption and subsumption errors henceforth having undesirable potential of consecutive misunderstandings in social and operational methodologies. The purpose of this study is to investigate an alternate forum for meta-data presentation that relies on humans’ strong pictorial perception capability. Analysis of big-data is assumed to be a complex and daunting task often reserved on the computational powers of machines yet there exist mapping tools which can analyze such data in a hand-handled manner. Data analysis on such scale can benefit from the use of statistical tools like Karnaugh maps where all studies can be put together on a graph based mapping. Such a formulation can lead to more control in observing patterns of research community and analyzing further for uncertainty and reliability metrics. We present a methodological process of converting a well-established study in Health care to its equaling binary representation followed by furnishing values on to a Karnaugh Map. The data used for the studies presented herein is from Burns et al (J Publ Health 34(1):138–148, ) consisting of retrospectively collected data sets from various studies on clinical coding data accuracy. Using a customized filtration process, a total of 25 studies were selected for review with no, partial, or complete knowledge of six independent variables thus forming 64 independent cells on a Karnaugh map. The study concluded that this pictorial graphing as expected had helped in simplifying the overview of meta-analysis and systemic reviews.
机译：长期以来，研究荟萃分析和系统评价已帮助我们总结了许多平行或矛盾的研究。现有研究以表格形式列出，其中包含针对特定案例的适当信息，但很难形象化。在数据的荟萃分析中，这可能导致吸收和吸收误差，因此，在社交和操作方法学上可能会产生连续误解的不良可能性。这项研究的目的是研究依赖于人类强大的图像感知能力的元数据表示的替代论坛。假定大数据分析是一项复杂而艰巨的任务，通常要保留在机器的计算能力上，但是存在可以以手动方式分析此类数据的映射工具。这种规模的数据分析可以受益于统计工具的使用，例如卡诺图，其中所有研究都可以放在基于图的地图上。这样的表述可以导致对研究团体的观察模式进行更多控制，并进一步分析不确定性和可靠性指标。我们提出了一种方法论过程，将一个完善的医疗保健研究转换为相等的二进制表示形式，然后将值提供给卡诺地图。本文提供的用于研究的数据来自Burns等人（J Publ Health 34（1）：138–148，），该数据由有关临床编码数据准确性的各种研究的回顾性收集数据集组成。使用定制的过滤过程，总共选择了25项研究以不了解，不了解或不完全了解六个独立变量，从而在卡诺图上形成64个独立细胞。该研究得出的结论是，这种预期的图形化图表有助于简化荟萃分析和系统评价的概述。

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