Name
Abstract | ||
|---|---|---|
The Electronic Health Record EHR groups all digital documents related to a given patient such as anamnesis, results of the laboratory tests, prescriptions, recorded medical signals as ECG or images, etc. Dealing with such data representation incurs a plethora of problems, such as different data types, even unstructured data i.e., doctor’s notes, huge and fast-growing volume, etc. Therefore. EHR should be considered as one of the most complex data objects in the information processing industry. Accordingly, taking into consideration its complexity, heterogeneity, fast growth, and size, the analysis of EHR data increasingly needs big data tools. Such tools should be able to analyze datasets characterized by the so-called 4Vs volume, velocity, variety, and veracity. These notwithstanding, we should also add the fifth V—value—because analytics tool deployment makes sense only if it leads to health-care improvement as personalized patient care, decreasing unnecessary hospitalization, or reducing patient readmissions. In this study, we focus on the selected aspects of EHR analysis from the big data perspective.
The Electronic Health Record EHR groups all digital documents related to a given patient such as anamnesis, results of the laboratory tests, prescriptions, recorded medical signals as ECG or images, etc. Dealing with such data representation incurs a plethora of problems, such as different data types, even unstructured data i.e., doctor’s notes, huge and fast-growing volume, etc. Therefore. EHR should be considered as one of the most complex data objects in the information processing industry. Accordingly, taking into consideration its complexity, heterogeneity, fast growth, and size, the analysis of EHR data increasingly needs big data tools. Such tools should be able to analyze datasets characterized by the so-called 4Vs volume, velocity, variety, and veracity. These notwithstanding, we should also add the fifth V—value—because analytics tool deployment makes sense only if it leads to health-care improvement as personalized patient care, decreasing unnecessary hospitalization, or reducing patient readmissions. In this study, we focus on the selected aspects of EHR analysis from the big data perspective.
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Year | DOI | Venue |
|---|---|---|
2016 | 10.1080/08839514.2016.1193714 | Applied Artificial Intelligence |
Field | DocType | Volume |
Data science,Data mining,Information processing,Software deployment,External Data Representation,Computer science,Unstructured data,Data type,Medical record,Analytics,Big data | Journal | 30 |
Issue | ISSN | Citations |
6 | 0883-9514 | 2 |
PageRank | References | Authors |
0.39 | 59 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boguslaw Cyganek | 1 | 145 | 24.53 |
| Manuel Graña | 2 | 1367 | 156.11 |
| Bartosz Krawczyk | 3 | 721 | 60.97 |
| Andrzej Kasprzak | 4 | 88 | 20.35 |
| Piotr Porwik | 5 | 181 | 21.52 |
| Krzysztof Walkowiak | 6 | 450 | 59.98 |
| Michal Wozniak | 7 | 764 | 83.90 |