Advanced patient matching: Recognizable patient view for decision support in healthcare using big data analytics

Like Air, The whole world is bounded by the data in the current day. The volume of open information and data explosion, Healthcare industries in on a critical moment. Here Big Data plays an important role for these new changes in health care. Nowadays Healthcare industries are suffering from many pr...

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Vydané v:ICTUS : 2017 International Conference on Infocom Technologies and Unmanned Systems (trends and future directions): December 18-20, 2017, Amity University Dubai, Dubai International Academic City s. 652 - 656
Hlavní autori: Johri, Prashant, Singh, Tanya, Yadav, Ashok, Rajput, Anil Kumar
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.12.2017
Predmet:
Big Data
Big Data Analytic
Diseases
Fuzzy Matching Algorithm
Hadoop
Healthcare Decision Support
Hospitals
Industries
Jaro-Winkler Distance
MapReduce
Organizations
Patient Matching
Pattern matching
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Shrnutí:Like Air, The whole world is bounded by the data in the current day. The volume of open information and data explosion, Healthcare industries in on a critical moment. Here Big Data plays an important role for these new changes in health care. Nowadays Healthcare industries are suffering from many problems because of data growth is speedily growing day by day. Healthcare decision support systems necessity a recognizable view of the patients for making the efficient decision against the patients and offer a better diagnosis and treatment. Patient pattern matching and identification is one of the biggest challenges in integrate to electronic healthcare record (EHR). The health care systems have the diverse document and result from unrelated systems like pharmacy, clinical, laboratory, insurance etc. we need to be matched with the fair patient record. This paper emphasizes on the challenge of patient matching records from diverse systems and offers a solution using Big Data analytic and pattern matching algorithm like Fuzzy Algorithm.
DOI:10.1109/ICTUS.2017.8286089