Potential and Pitfalls for Big Data in Health Research

Ladha, Karim; Arora, Vishal S.; Dutton, Richard P.; Hyder, Joseph A.

doi:10.1016/j.aan.2015.07.006

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Analysis and big data are used in healthcare programs other than post‐abnormal EHRs, for example, traditional inpatient and WHMS). Internal and external health monitoring systems include a variety of sensors, actuators, and multimedia devices that constantly monitor health, function, movement, and mood at low cost 10 . Health monitoring clothing is essential for data mining and continuous analysis of various types of physiological data 11,12 .…”

Section: Introductionmentioning

confidence: 99%

“…Internal and external health monitoring systems include a variety of sensors, actuators, and multimedia devices that constantly monitor health, function, movement, and mood at low cost. 10 Health monitoring clothing is essential for data mining and continuous analysis of various types of physiological data. 11,12 Such systems can reduce health costs through immunization and improve quality of life with better diagnosis.…”

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confidence: 99%

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Hybrid optimization based learning technique for multi‐disease analytics from healthcare big data using optimal pre‐processing, clustering and classifier

Parimanam

Lakshmanan

Palaniswamy

2022

Concurrency and Computation

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With the rapid development of health information systems in recent years covering big data trends, clinical information is being adapted to understand important health trends and provide timely prophylactic treatment. However, the current plan fails to maintain a highly organized analysis and processing of health data. In addition, it collects issues that play an important role in determining quality. In this article, we propose a hybrid optimization based learning technique for multi‐disease analytics (HOL‐MD) from healthcare big data using optimal pre‐processing, clustering, and classifier. First, we introduce a capuchin search based optimization algorithm for preprocessing which removes the unwanted artifacts to enhance the detection accuracy. Second, we introduce a modified Harris Hawks optimization based clustering (MHHOC) technique used to select optimal features among multiple features which discovers the subgroups and reduce the dimensionality issues. We evaluated the performance of proposed HOL‐MD technique using standard US healthcare organization dataset, SUSY and HIGGS datasets. Finally, we proved the effectiveness of proposed RTL‐DNN techniques in terms of 11.5%, 9.3%, 2.75%, and 3.45% higher than the existing state‐of‐art techniques in terms of accuracy, precision, recall, and F‐measure respectively.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Hybrid optimization based learning technique for multi‐disease analytics from healthcare big data using optimal pre‐processing, clustering and classifier

Parimanam

Lakshmanan

Palaniswamy

2022

Concurrency and Computation

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“…Surveys on big data and healthcare [1,[4][5][6][7] Healthcare data mining/health informatics [8,9] Disease management [10,11] Telemedicine, e-health, m-health [12] Prediction of pathologies/digital epidemiology [13,14] Medical research [15] Identification and classification of at-risk people [16] Global health [17] Determination of patients' acuity levels [18] Detection of fraud [19] Evaluation of treatment effectiveness [10] Improvement of health outcomes [20] Cutting healthcare costs-Treatment optimization plans [21] Enhancement of patients' experiences [22] Precision medicine-prevention and treatment strategies that take individual variability into account-personalized medicine [23,24] Healthcare operations management/supply chain/capacity planning-scheduling-control-design of care-delivery systems-quality issues [25] Total project-financed investment grew by a factor of 10 times in a decade: From USD 41.3 billion in 1994 to USD 415 billion in 2013. Esty et al [26] report that a record USD 57.8 billion in Project Financing funding was arranged in Western Europe (W.E.)…”

Section: Introductionmentioning

confidence: 99%

Big Data for the Sustainability of Healthcare Project Financing

Visconti

Morea

2019

Sustainability

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This study aims to detect if and how big data can improve the quality and timeliness of information in infrastructural healthcare Project Finance (PF) investments, making them more sustainable, and increasing their overall efficiency. Interactions with telemedicine or disease management and prediction are promising but are still underexploited. However, given rising health expenditure and shrinking budgets, data-driven cost-cutting is inevitably required. An interdisciplinary approach combines complementary aspects concerning big data, healthcare information technology, and PF investments. The methodology is based on a business plan of a standard healthcare Public-Private Partnership (PPP) investment, compared with a big data-driven business model that incorporates predictive analytics in different scenarios. When Public and Private Partners interact through networking big data and interoperable databases, they boost value co-creation, improving Value for Money and reducing risk. Big data can also help by shortening supply chain steps, expanding economic marginality and easing the sustainable planning of smart healthcare investments. Flexibility, driven by timely big data feedbacks, contributes to reducing the intrinsic rigidity of long-termed PF healthcare investments. Healthcare is a highly networked and systemic industry, that can benefit from interacting with big data that provide timely feedbacks for continuous business model re-engineering, reducing the distance between forecasts and actual occurrences. Risk shrinks and sustainability is fostered, together with the bankability of the infrastructural investment.

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Big Data in Health: a Literature Review from the Year 2005

et al. 2016

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The information stored in healthcare systems has increased over the last ten years, leading it to be considered Big Data. There is a wealth of health information ready to be analysed. However, the sheer volume raises a challenge for traditional methods. The aim of this article is to conduct a cutting-edge study on Big Data in healthcare from 2005 to the present. This literature review will help researchers to know how Big Data has developed in the health industry and open up new avenues for research. Information searches have been made on various scientific databases such as Pubmed, Science Direct, Scopus and Web of Science for Big Data in healthcare. The search criteria were "Big Data" and "health" with a date range from 2005 to the present. A total of 9724 articles were found on the databases. 9515 articles were discarded as duplicates or for not having a title of interest to the study. 209 articles were read, with the resulting decision that 46 were useful for this study. 52.6 % of the articles used were found in Science Direct, 23.7 % in Pubmed, 22.1 % through Scopus and the remaining 2.6 % through the Web of Science. Big Data has undergone extremely high growth since 2011 and its use is becoming compulsory in developed nations and in an increasing number of developing nations. Big Data is a step forward and a cost reducer for public and private healthcare.

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Potential and Pitfalls for Big Data in Health Research

Cited by 4 publications

References 21 publications

Hybrid optimization based learning technique for multi‐disease analytics from healthcare big data using optimal pre‐processing, clustering and classifier

Hybrid optimization based learning technique for multi‐disease analytics from healthcare big data using optimal pre‐processing, clustering and classifier

Big Data for the Sustainability of Healthcare Project Financing

Big Data in Health: a Literature Review from the Year 2005

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