Pattern-based Mining in Electronic Health Records for Complex Clinical Process Analysis

Metsker, Oleg G.; Bolgova, E. V.; Yakovlev, A. N.; Funkner, Anastasia A.; Kovalchuk, Sergey V.

doi:10.1016/j.procs.2017.11.177

Cited by 21 publications

(9 citation statements)

References 11 publications

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“…Moreover, text mining professionals are increasingly becoming high in demand. Furthermore, text mining may have the power to deliver significant insights to society and individuals, especially with respect to public health [258,259], healthcare [260,261], and education [262][263][264][265], and help evaluate social issues, such as crime (including cybercrime) [245,266,267], child abuse [268], and poverty [269]. Nevertheless, actions must be taken in time to efficiently solve the legal, ethical, and privacy concerns contained in the use of personal data.…”

Section: Discussionmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

188

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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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Section: Discussionmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

188

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“…Extracting the relevant information [64] Syntactic complexity, abbreviations, domainspecific terms, and semantic complexity Syntactic & semantic structure Finding relevant data and automatic transformation [65] Screening and identification of relevant information Subjective prescreening criteria design Semantic relatedness [66] Information uncertainty and data heterogeneity Context-sensitive semantic relatedness Data transparency [72] Insufficient structured metadata and attributes Data mapping [68] Integration of textual information with traditional data Lattice structure for domain context Domain specificity in hierarchical structuring [56] Structural ambiguity (semantic + syntactic structure)…”

Section: Transformationmentioning

confidence: 99%

“…Each usability issue with challenges has been presented in Table 6. [11], finding the right data [24] [70], variation in perspectives [24], concept identification [71], insufficient structural metadata [72] [81], lack of dynamic context [19], data complexity [64], diversity [53], and uncertainty [66] Completeness Contextual variability of relationships [67], usability and semantic relationship [54], inherent precision and uncertainty factors [73], data causality issues [55], structural ambiguities text [56] [58], readability [57], lack of structure [68] [32] [74] [80], structural differences [60], and data incompleteness [11] 2) KEY PROCESSES…”

Section: ) Usability Issuesmentioning

confidence: 99%

Development of Usability Enhancement Model for Unstructured Big Data Using SLR

2021

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Unstructured text contains valuable information for a range of enterprise applications and informed decision making. Text analytics is used to extract valuable insights from unstructured big data. Among the most significant challenges of text analytics, quality and usability are critical affecting the outcome of the analytical process. The enhancement in usability is important for the exploitation of unstructured data. Most of the existing literature focuses on the usability of structured data as compared to unstructured data whereas big data usability has been discussed merely in context of its assessment. The existing approaches do not provide proper guidelines on usability enhancement of unstructured data. In this study, a rigorous systematic literature review, using PRISMA framework, has been conducted to develop a model enhancing the usability of unstructured data and bridging the research gap. The recent approaches and solutions for text analytics have been investigated thoroughly. Furthermore, it identifies the usability issues of unstructured text data and their consequences on data preparation for analytics. Defining the usability dimensions for unstructured big data, identification of the usability determinants, and developing a relationship between usability dimension and determinants to derive usability rules are the significant contribution of this research, and are integrated to formulate the model. The proposed usability enhancement model is the major outcome of the study. It would contribute to making unstructured data usable and facilitating the data preparation activities with more valuable data that eventually improves the analytical process.

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“…Comparison of various leprosy cases all over the world and its awareness [11]. The problem of knowledge retrieval from EHR and interpreting medical records and applying various algorithms using text mining [14]. Text mining is done on electronic patient records to get the proper preprocessed data and how labeling ,classifiers and feature extraction is performed on the EPR.…”

Section: Literature Reviewmentioning

confidence: 99%

Clinical Text Mining of Electronic Health Records to Classify Leprosy Patients Cases

Mehta,

Dharamsey,

Domal

et al. 2020

IJITEE

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Leprosy is one of the major public health problems and listed among the neglected tropical diseases in India. It is also called Hansen's Diseases (HD), which is a long haul contamination by microorganisms Mycobacterium leprae or Mycobacterium lepromatosis.Untreated, leprosy can dynamic and changeless harm to the skin, nerves, appendages, and eyes. This paper intends to depict classification of leprosy cases from the main indication of side effects. Electronic Health Records (EHRs) of Leprosy Patients from verified sources have been generated. The clinical notes included in EHRs have been processed through Natural Language Processing Tools. In order to predict type of leprosy, Rule based classification method has been proposed in this paper. Further our approach is compared with various Machine Learning (ML) algorithms like Support Vector Machine (SVM), Logistic regression (LR) and performance parameters are compared.

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Pattern-based Mining in Electronic Health Records for Complex Clinical Process Analysis

Cited by 21 publications

References 11 publications

Text Mining in Big Data Analytics

Text Mining in Big Data Analytics

Development of Usability Enhancement Model for Unstructured Big Data Using SLR

Clinical Text Mining of Electronic Health Records to Classify Leprosy Patients Cases

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