Improving patient matching: Single patient view for Clinical Decision Support using Big Data analytics

Duggal, Ritu; Khatri, Sunil Kumar; Shukla, Balvinder

doi:10.1109/icrito.2015.7359269

Cited by 19 publications

(23 citation statements)

References 2 publications

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“…In another interesting application in the field of medicine Duggal et al utilized fuzzy logic based matching algorithms and MapReduce in order to perform Big Data analytics for clinical decision support. Their developed system has demonstrated great flexibility and was able to handle data from various sources (Duggal et al, 2015).…”

Section: Computational Intelligence For Big Data Analyticsmentioning

confidence: 99%

Big data analytics: Computational intelligence techniques and application areas

Iqbal

Doctor

et al. 2020

Technological Forecasting and Social Change

176

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Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment.

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Section: Computational Intelligence For Big Data Analyticsmentioning

confidence: 99%

Big data analytics: Computational intelligence techniques and application areas

Iqbal

Doctor

et al. 2020

Technological Forecasting and Social Change

176

View full text Add to dashboard Cite

show abstract

“…However steps have been implemented in a bid to tackle this challenge, for eg the Enterprise Master Patient Index(EMPI) uses a variety of statistical algorithms to match patient records while striving to minimize the abundance of false positive and false negative matches [17] Even with the existence of such systems there exists a huge amount of duplicate records.…”

Section: A Lack Of Nationwide Unique Identifiermentioning

confidence: 99%

“…Similarity can be assessed by calculating distance between 2 pieces of data information, the more dissimilar are the two sets of data. To tackle the enormity and the monstrosity of data and the speed at which it is incoming, MapReduce is implemented using Levenshtein Distance algorithm [17].Levenshtein distance is the distance between two words which is the minimum number of single character edits needed to make one word same as the other .MapReduce can be declared as a platform which is a programming model based on different distributed computations on huge amounts of data which includes execution framework for the processing function on huge clusters of commodity servers. So, the basic function of MapReduce is to match patients' data which basically entails multiple attributes of a patient identity namely Birth Address, City, Pin Code so on and so forth.…”

Section: E Data Analyticsmentioning

confidence: 99%

Big Data: Healthcare Informatics

Shukla¹

2017

IJRASET

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Collection, storage and analysis of health data have been, are and will be one of the cornerstone to provide efficient healthcare services and its importance is only multiplying considering the vast amount of health data collected every day. Knowledge discovery from various health data repositories requires the incorporation of healthcare data from diversified sources. While the healthcare industry is going digital ,the traditional and modern techniques of collection of data are facing mammoth challenges emerging due to the massive influx of data which is a direct result of population explosion. One thing which is certain in this ever-changing environment is that the advancement of technology goes hand in hand with monumental progress in the medical field. Revolutionary innovations and research have led to crucial strides being made in healthcare leading to life changing applications in providing diagnosis and treatments for diseases like cancer and chronic conditions such as hypertension to name a few. This paper covers the various hindrances faced by the healthcare industry in all in all its spheres and the possible solutions for the same.

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“…In another interesting application in the field of medicine, Duggal et al utilized fuzzy logic based matching algorithms and MapReduce in order to perform Big Data analytics for clinical decision support. The developed system demonstrated great flexibility and was able to handle data from various sources (Duggal, Khatri, & Shukla, 2015).…”

Section: Computational Intelligence For Big Data Analyticsmentioning

confidence: 99%

TEMPORARY REMOVAL: Big Data analytics: Computational intelligence techniques and application areas

Iqbal

Doctor

et al. 2016

International Journal of Information Management

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a b s t r a c tBig Data has a significant impact in modern society. In this paper we investigated the importance of Big Data in modern life, and in terms of the economy, and discussed the challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explored the potential of the powerful combination of Big Data and Computational intelligence and identified a number of areas where novel applications in real world problems can be developed by utilizing these powerful tools and technologies. We presented a novel data modelling methodology which introduces a novel biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). In this paper, we have also discussed various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment.

show abstract

Improving patient matching: Single patient view for Clinical Decision Support using Big Data analytics

Cited by 19 publications

References 2 publications

Big data analytics: Computational intelligence techniques and application areas

Big data analytics: Computational intelligence techniques and application areas

Big Data: Healthcare Informatics

TEMPORARY REMOVAL: Big Data analytics: Computational intelligence techniques and application areas

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