Knowledge discovery from massive healthcare claims data

Chandola, Varun; Sukumar, Sreenivas R.; Schryver, Jack C.

doi:10.1145/2487575.2488205

Cited by 87 publications

(36 citation statements)

References 19 publications

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“…American Healthways used patient information to predict the likelihood of short-term health problems and provided intervention to achieve better short-term and long-term results [30]. In [31] more sophisticated data mining algorithms were used to improve healthcare operations and reducing fraud, waste, and abuse. The analysis for the case studies described in [31] was using the Hadoop/Hive data platform and open source software such as Mahout, R, and Python networkx.…”

Section: B Data Analysis Methods and Software Toolsmentioning

confidence: 99%

“…In [31] more sophisticated data mining algorithms were used to improve healthcare operations and reducing fraud, waste, and abuse. The analysis for the case studies described in [31] was using the Hadoop/Hive data platform and open source software such as Mahout, R, and Python networkx. Specifically, topic modelling was performed on the a year of claim data and 20 hidden topics were revealed, which can be used in identifying the costly areas which need to be addressed and in comparing providers to identify fraudulent or wasteful providers.…”

Section: B Data Analysis Methods and Software Toolsmentioning

confidence: 99%

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Protecting and Analysing Health Care Data on Cloud

Thilakanathan

Zhao

Chen

et al. 2014

2014 Second International Conference on Advanced Cloud and Big Data

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Various health care devices owned by either hospitals or individuals are producing huge amount of health care data. The big health data may contain valuable knowledge and new business opportunities. Obviously, cloud is a good candidate to collect, store and analyse such big health care data. However, health care data is very sensitive for its owners, and thus should be well protected on cloud. This paper presents our solution to protecting and analyzing health care data stored on cloud. First, we develop novel technologies to protect data privacy and enable secure data sharing on cloud. Secondly, we show the methods and tools to conduct big health care data analysis. Finally, both the security technology and the data analysis methods are evaluated to show the usefulness and efficiency of our solution.

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Section: B Data Analysis Methods and Software Toolsmentioning

confidence: 99%

Section: B Data Analysis Methods and Software Toolsmentioning

confidence: 99%

Protecting and Analysing Health Care Data on Cloud

Thilakanathan

Zhao

Chen

et al. 2014

2014 Second International Conference on Advanced Cloud and Big Data

View full text Add to dashboard Cite

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“…Varun Chandola et al [12] in his work did analysis of the health care data using social network analysis, temporal analysis and text mining and higher order feature construction to understand how each of these areas contributes to understand the domain of healthcare. Temporal Analysis methods are used as they do not require trained classifier to identify anomalies and it can be used as a timely technique for detection of transient billing practices that are anomalous.…”

Section: Related Workmentioning

confidence: 99%

Big Data solutions in Healthcare: Problems and perspectives

Mathew

Pillai

2015

2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

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Data in the healthcare sector is growing beyond dealing capacity of the health care organizations and is expected to increase significantly in the coming years. Majority of the Healthcare data is often unstructured, exists in silos and resides in imaging systems, medical prescription notes, insurance claims data, EPR (Electronic Patient Records) etc. integrating these heterogeneous data and factoring it in to advance analytics is critical to improve healthcare outcomes. Either because data are isolated in disparate or incompatible formats or due to the lack in processing capability to load and query large datasets in a timely fashion the Healthcare organizations are not in a position to leverage the benefits of the vast data they have. With convergence of advanced computing and numerous Big Data technological options like commercial solutions, Open Source, Cloud etc. it is now possible to attain high performance, scalability at a relatively low cost. Big data solutions often come with set of innovative data management solutions and analytical tools, when effectively implemented can transform the healthcare outcomes.

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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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Knowledge discovery from massive healthcare claims data

Cited by 87 publications

References 19 publications

Protecting and Analysing Health Care Data on Cloud

Protecting and Analysing Health Care Data on Cloud

Big Data solutions in Healthcare: Problems and perspectives

Intelligent Healthcare Systems Assisted by Data Analytics and Mobile Computing

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