General analytics limitations with coronavirus healthcare big data

IF 0.4 Q4 HEALTH CARE SCIENCES & SERVICES
K. Strang
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引用次数: 1

Abstract

Search engines and the SPSS Python R extension were used to analyse COVID-19 healthcare big data information stored on the internet to identify significant limitations of statistical techniques. The sample was a manageable subset of dynamic information from the internet time-stamped to midnight of 14 April, 2020 with a filter set for coronavirus confirmed cases or deaths in Wuhan Hubei province in China, New York State in USA and New South Wales, Australia. There were surprising results, indicating using general analytics that the healthcare big data were not reliable. Interesting relationships were detected when linking Australian foreign property ownership to the cities experiencing the largest coronavirus related fatalities.
冠状病毒医疗保健大数据的一般分析限制
使用搜索引擎和SPSS Python R扩展对存储在互联网上的COVID-19医疗保健大数据信息进行分析,以确定统计技术的重大局限性。该样本是一个可管理的动态信息子集,这些信息来自互联网,时间戳为2020年4月14日午夜,并为中国湖北省武汉市、美国纽约州和澳大利亚新南威尔士州的冠状病毒确诊病例或死亡设置了过滤器。令人惊讶的结果表明,使用一般分析方法,医疗保健大数据并不可靠。当将澳大利亚的外国房产所有权与冠状病毒相关死亡人数最多的城市联系起来时,发现了有趣的关系。
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来源期刊
CiteScore
1.00
自引率
10.00%
发文量
10
期刊介绍: IJHTM is a new series emerging from the International Journal of Technology Management. It provides an international forum and refereed authoritative sources of information in the fields of management, economics and the management of technology in healthcare.
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