A Big Data Architecture for the Extraction and Analysis of EHR Data

Silvestri, Stefano; Esposito, Angelo; Gargiulo, Francesco; Sicuranza, Mario; Ciampi, Mario; Pietro, Giuseppe De

doi:10.1109/services.2019.00082

Cited by 16 publications

(10 citation statements)

References 16 publications

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“…The average CMIs for the training and test sets are 3.22% and 18.98% for English and Chinese, respectively. Comparing with the CMI values in the previous study [4], our average CMI (11.1) is higher, which should bring challenges in the task of de-identification.…”

Section: A Statistics Of the Code-mixing Deidentification Corpuscontrasting

confidence: 56%

“…As one can see from Table 2, the PHIs in our corpus could be described in monolingual (English or Chinese) and bilingual (English-Chinese mixed) sentences. We therefore applied the code-mixing index (CMI) defined by [4] to measure the mixing level of our corpus. We calculated separated CMIs for English and Chinese while the sentences were dominated one language.…”

Section: A Statistics Of the Code-mixing Deidentification Corpusmentioning

confidence: 99%

“…According to the statistics of the electronic medical record exchange center in 2016, 411 of 496 hospitals (80.4%) and about 5,244 of 9,782 private clinics (53.6%) in Taiwan were certified as having interoperable EHRs [3]. Consequently, the world now has a tremendous volume of EHRs conveyed significant health care information which lead led healthcare providers to adopt various approaches to extract and analyze EHR data for secondary use in particular from un-structured texts [4].…”

Section: Introductionmentioning

confidence: 99%

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Principle-Based Approach for the De-Identification of Code-Mixed Electronic Health Records

Wang

Lee

et al. 2022

IEEE Access

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Code-mixing is a phenomenon when at least two languages combined in a hybrid way in the context of a single conversation. The use of mixed language is widespread in multilingual and multicultural countries and poses significant challenges for the development of automated language processing tools. In Taiwan's electronic health record (EHR) systems, the unstructured EHR texts are usually represented in the mixing of English and Chinese languages resulting in the difficulty for de-identification and synthetization of protected health information (PHI). We explored this problem by applied several state-of-the-art pretrained mono-and multilingual language models and proposed to apply the principle-based approach (PBA) for the tasks of PHI recognition and resynthesis on a code-mixed EHR corpus, which was annotated with 6 main categories and 25 subcategories of PHIs. In PBA, a hierarchical principle slot schema is defined to encode knowledge of code-mixed PHIs and the defined slots were learned from the training set to assemble into principles for recognizing PHI mentions and synthesizing surrogates at the same time. A semantic disambiguation process is developed used to disambiguate ambiguous PHI categories in the de-identification process and to dynamically extend the knowledge encoded in PBA during the knowledge augmentation process. The experimental results demonstrate that the proposed method can achieve the best micro-and macro-F-scores performance in comparison with the other mono-and multilingual language models finetuned on our code-mixed corpus.

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Section: A Statistics Of the Code-mixing Deidentification Corpuscontrasting

confidence: 56%

Section: A Statistics Of the Code-mixing Deidentification Corpusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Principle-Based Approach for the De-Identification of Code-Mixed Electronic Health Records

Wang

Lee

et al. 2022

IEEE Access

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“…Combining big data analytics and cloud computing can help reduce energy consumption [81]. For example, developing energy-efficient systems [82] and application architecture such as virtual data storage and servers replacing traditional IT infrastructure can lower IT power consumption [83]. Besides, the traditional data systems are significantly reliant on paperwork, adversely impacting forests and the environment.…”

Section: Opportunities Of Big Data In Health Carementioning

confidence: 99%

Impact of Big Data Analysis on Health

Rosário¹,

Dias²

2022

Preprint

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Big data analytics tools are the use of advanced analytic techniques targeting large and diverse volumes of data that include structured, semi-structured, and unstructured data from different sources and in different sizes from terabytes to zetabytes. The health sector is faced with the need to generate and manage large data sets from various health systems, such as electronic health records and clinical decision support systems. This data can be used by providers, clinicians, and policymakers to plan and implement interventions, detect disease more quickly, predict outcomes, and personalize care delivery. However, little attention is paid to the connection between big data analytics tools and the health sector. Thus, a systematic review of the bibliometric literature (LRSB) was developed to study how the adoption of big data analytics tools and infrastructures will revolutionize the healthcare industry. The review integrated 77 scientific and/or academic documents indexed in SCOPUS presenting up‐to‐date knowledge on current insights on how big data analytics technologies influence the healthcare sector and the different big data analytical tools used. The LRSB provides findings related to the impact of Big Data analytics on the health sector by introducing opportunities and technologies that provide practical solutions to various challenges.

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“…In a single, structured way while considering data responsiveness, privacy can hamper, and ethical concerns raise [5]. Digital evidence should be transformed into a unified, standardized, and codified representation using modern methods such as big data analysis [6]. Proper use of this data requires the use of appropriate anonymization procedures.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Big Data Privacy Preservation Technique for Electronic Health Records in Multivendor Environment

Puri¹,

Haritha²

2023

IJACSA

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Various diagnostic health data formats and standards include both structured and unstructured data. Sensitive information contained in such metadata requires the development of specific approaches that can combine methods and techniques that can extract and reconcile the information hidden in such data. However, when this data needs to be processed and used for other reasons, there are still many obstacles and concerns to overcome. Modern approaches based on machine learning including big data analytics, assist in the information refinement process for later use of clinical evidence. These strategies consist of transforming various data into standard formats in specific scenarios. In fact, in order to conform to these rules, only de-identified diagnostic and personal data may be handled for secondary analysis, especially when information is distributed or transferred across institutions. This paper proposes big data privacy preservation techniques using various privacy functions. This research focused on secure data distribution as well as security access control to revoke the malicious activity or similarity attacks from end-user. The various privacy preservation techniques such as data anonymization, generalization, random permutation, kanonymity, bucketization, l-diversity with slicing approach have been proposed during the data distribution. The efficiency of system has been evaluated in Hadoop distributed file system (HDFS) with numerous experiments. The results obtained from different experiments show that the computation should be changed when changing k-anonymity and l-diversity. As a result, the proposed system offers greater efficiency in Hadoop environments by reducing execution time by 15% to 18% and provides a higher level of access control security than other security algorithms.

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A Big Data Architecture for the Extraction and Analysis of EHR Data

Cited by 16 publications

References 16 publications

Principle-Based Approach for the De-Identification of Code-Mixed Electronic Health Records

Principle-Based Approach for the De-Identification of Code-Mixed Electronic Health Records

Impact of Big Data Analysis on Health

Implementation of Big Data Privacy Preservation Technique for Electronic Health Records in Multivendor Environment

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