Managing evolving code sets and integration of multiple data sources in health care analytics

McGlothlin, James P.; Khan, Latifur

doi:10.1145/2512410.2512424

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…When the company stakeholders are able to make fact-based decision using the capability of data analytics, the company will have a greater likelihood of increased revenue, cost-cutting, and improved competitive advantage. To date, data analytics has been widely adopted to support the operations in many businesses or industries like healthcare (McGlothlin and Khan 2013), electricity supply (Qiu et al 2013), manufacturing (Jesus and Bernardino 2014), railway safety management (Lira et al 2014), financial service (Chang 2014), tourism (Rebón et al 2015), education (Haupt et al 2015), monitoring of quality for web services , monitoring for quality-of-experience in high-density wireless network (Qaiyum et al 2016) and even not-for-profit organizations (Oakley et al 2015). Figure 1 depicts the entire implementation process of a typical data analytics practice (Gullo 2015).…”

Section: Introductionmentioning

confidence: 99%

Algorithms for frequent itemset mining: a literature review

Chee¹,

Jaafar²,

Aziz³

et al. 2018

Artif Intell Rev

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Data Analytics plays an important role in the decision making process. Insights from such pattern analysis offer vast benefits, including increased revenue, cost cutting, and improved competitive advantage. However, the hidden patterns of the frequent itemsets become more time consuming to be mined when the amount of data increases over the time. Moreover, significant memory consumption is needed in mining the hidden patterns of the frequent itemsets due to a heavy computation by the algorithm. Therefore, an efficient algorithm is required to mine the hidden patterns of the frequent itemsets within a shorter run time and with less memory consumption while the volume of data increases over the time period. This paper reviews and presents a comparison of different algorithms for Frequent Pattern Mining (FPM) so that a more efficient FPM algorithm can be developed.

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Section: Introductionmentioning

confidence: 99%

Algorithms for frequent itemset mining: a literature review

Chee¹,

Jaafar²,

Aziz³

et al. 2018

Artif Intell Rev

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“…[15][16][17]. In [18], it discussed clear motivations and advantages of multisensor data fusion and particularly focuses on physical activity recognition, aiming at providing a systematic categorization and common comparison framework of the literature, by identifying distinctive properties and parameters affecting data fusion design choices at different levels (data, feature, and decision).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Healthcare Systems Assisted by Data Analytics and Mobile Computing

Wang²,

Zhou

et al. 2018

2018 14th International Wireless Communications &Amp; Mobile Computing Conference (IWCMC)

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It is entering an era of big data, which facilitated great improvement in various sectors. Particularly, assisted by wireless communications and mobile computing, mobile devices have emerged with a great potential to renovate the healthcare industry. Although the advanced techniques will make it possible to understand what is happening in our body more deeply, it is extremely difficult to handle and process the big health data anytime and anywhere. Therefore, data analytics and mobile computing are significant for the healthcare systems to meet many technical challenges and problems that need to be addressed to realize this potential. Furthermore, the advanced healthcare systems have to be upgraded with new capabilities such as machine learning, data analytics, and cognitive power for providing human with more intelligent and professional healthcare services. To explore recent advances and disseminate state-of-the-art techniques related to data analytics and mobile computing on designing, building, and deploying novel technologies, to enable intelligent healthcare services and applications, this paper presents the detailed design for developing intelligent healthcare systems assisted by data analytics and mobile computing. Moreover, some representative intelligent healthcare applications are discussed to show that data analytics and mobile computing are available to enhance the performance of the healthcare services.

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