Big Data fraud detection using multiple medicare data sources

This chapter proposes a new analytical approach that consolidates the traditional analytical approach for solving problems such as churn detection, fraud detection, building predictive models, segmentation modeling with data sources, and analytical techniques from the big data area. Presented are solutions offering a structured approach for the integration of different concepts into one, which helps analysts as well as managers to use potentials from different areas in a systematic way. By using this concept, companies have the opportunity to introduce big data potential in everyday data mining projects. As is visible from the chapter, neglecting big data potentials results often with incomplete analytical results, which imply incomplete information for business decisions and can imply bad business decisions. The chapter also provides suggestions on how to recognize useful data sources from the big data area and how to analyze them along with traditional data sources for achieving more qualitative information for business decisions.

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Proposal of Analytical Model for Business Problems Solving in Big Data Environment

Strategic Data-Based Wisdom in the Big Data Era - Advances in Knowledge Acquisition, Transfer, and Management ◽

10.4018/978-1-4666-8122-4.ch012 ◽

2015 ◽

pp. 209-228 ◽

Cited By ~ 7

Author(s):

Goran Klepac ◽

Kristi L. Berg

Keyword(s):

Data Mining ◽

Big Data ◽

Predictive Models ◽

Analytical Approach ◽

Fraud Detection ◽

Analytical Techniques ◽

Data Sources ◽

Business Decisions ◽

Mining Projects ◽

Structured Approach

This chapter proposes a new analytical approach that consolidates the traditional analytical approach for solving problems such as churn detection, fraud detection, building predictive models, segmentation modeling with data sources, and analytical techniques from the big data area. Presented are solutions offering a structured approach for the integration of different concepts into one, which helps analysts as well as managers to use potentials from different areas in a systematic way. By using this concept, companies have the opportunity to introduce big data potential in everyday data mining projects. As is visible from the chapter, neglecting big data potentials results often with incomplete analytical results, which imply incomplete information for business decisions and can imply bad business decisions. The chapter also provides suggestions on how to recognize useful data sources from the big data area and how to analyze them along with traditional data sources for achieving more qualitative information for business decisions.

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Big Data Driven Clinical Informatics & Surveillance (BDD_CIS) – A Multimodal Database Focused Clinical, Community, and Multi-Omics Surveillance Plan for COVID-19: A study Protocol (Preprint)

10.2196/preprints.24504 ◽

2020 ◽

Author(s):

Bankole Olatosi ◽

Jiajia Zhang ◽

Sharon Weissman ◽

Zhenlong Li ◽

Jianjun Hu ◽

...

Keyword(s):

Big Data ◽

South Carolina ◽

Data Science ◽

Age Groups ◽

The Elderly ◽

The United States ◽

Data Sources ◽

Patient Registries ◽

Multiple Partner ◽

Multimodal Data

BACKGROUND The Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) remains a serious global pandemic. Currently, all age groups are at risk for infection but the elderly and persons with underlying health conditions are at higher risk of severe complications. In the United States (US), the pandemic curve is rapidly changing with over 6,786,352 cases and 199,024 deaths reported. South Carolina (SC) as of 9/21/2020 reported 138,624 cases and 3,212 deaths across the state. OBJECTIVE The growing availability of COVID-19 data provides a basis for deploying Big Data science to leverage multitudinal and multimodal data sources for incremental learning. Doing this requires the acquisition and collation of multiple data sources at the individual and county level. METHODS The population for the comprehensive database comes from statewide COVID-19 testing surveillance data (March 2020- till present) for all SC COVID-19 patients (N≈140,000). This project will 1) connect multiple partner data sources for prediction and intelligence gathering, 2) build a REDCap database that links de-identified multitudinal and multimodal data sources useful for machine learning and deep learning algorithms to enable further studies. Additional data will include hospital based COVID-19 patient registries, Health Sciences South Carolina (HSSC) data, data from the office of Revenue and Fiscal Affairs (RFA), and Area Health Resource Files (AHRF). RESULTS The project was funded as of June 2020 by the National Institutes for Health. CONCLUSIONS The development of such a linked and integrated database will allow for the identification of important predictors of short- and long-term clinical outcomes for SC COVID-19 patients using data science.

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Methodology of Big Data Integration from A Priori Unknown Heterogeneous Data Sources

Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence - CSAI '18 ◽

10.1145/3297156.3297249 ◽

2018 ◽

Author(s):

Alexey Samoylov ◽

Nikolay Sergeev ◽

Margarita Kucherova ◽

Boris Denisov

Keyword(s):

Big Data ◽

Data Integration ◽

A Priori ◽

Heterogeneous Data ◽

Data Sources ◽

Heterogeneous Data Sources

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Introduction to the Special Issue on New Longitudinal Data for Retirement Analysis and Policy

Journal of Pension Economics and Finance ◽

10.1017/s1474747221000044 ◽

2021 ◽

pp. 1-5

Author(s):

Marco Angrisani ◽

Anya Samek ◽

Arie Kapteyn

Keyword(s):

Big Data ◽

Academic Research ◽

Cost Effective ◽

Research Data ◽

Data Sources ◽

Special Issue ◽

Administrative Records ◽

The Past ◽

Survey Questionnaires ◽

Internet Panels

The number of data sources available for academic research on retirement economics and policy has increased rapidly in the past two decades. Data quality and comparability across studies have also improved considerably, with survey questionnaires progressively converging towards common ways of eliciting the same measurable concepts. Probability-based Internet panels have become a more accepted and recognized tool to obtain research data, allowing for fast, flexible, and cost-effective data collection compared to more traditional modes such as in-person and phone interviews. In an era of big data, academic research has also increasingly been able to access administrative records (e.g., Kostøl and Mogstad, 2014; Cesarini et al., 2016), private-sector financial records (e.g., Gelman et al., 2014), and administrative data married with surveys (Ameriks et al., 2020), to answer questions that could not be successfully tackled otherwise.

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Mapping the United Nations Fundamental Principles of Official Statistics against new and big data sources

Statistical Journal of the IAOS ◽

10.3233/sji-210789 ◽

2021 ◽

Vol 37 (1) ◽

pp. 161-169

Author(s):

Dominik Rozkrut ◽

Olga Świerkot-Strużewska ◽

Gemma Van Halderen

Keyword(s):

Big Data ◽

Public Information ◽

Fundamental Principle ◽

Data Sources ◽

Official Statistics ◽

Development Agenda ◽

Data Gaps ◽

Data Source ◽

Exciting Time ◽

Statistical Systems

Never has there been a more exciting time to be an official statistician. The data revolution is responding to the demands of the CoVID-19 pandemic and a complex sustainable development agenda to improve how data is produced and used, to close data gaps to prevent discrimination, to build capacity and data literacy, to modernize data collection systems and to liberate data to promote transparency and accountability. But can all data be liberated in the production and communication of official statistics? This paper explores the UN Fundamental Principles of Official Statistics in the context of eight new and big data sources. The paper concludes each data source can be used for the production of official statistics in adherence with the Fundamental Principles and argues these data sources should be used if National Statistical Systems are to adhere to the first Fundamental Principle of compiling and making available official statistics that honor citizen’s entitlement to public information.

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