Clustering clinical models from local electronic health records based on semantic similarity

Gøeg, Kirstine Rosenbeck; Cornet, Ronald; Andersen, Stig Kjær

doi:10.1016/j.jbi.2014.12.015

Cited by 20 publications

(12 citation statements)

References 28 publications

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“…According to the literature, a lot of work is done on semantic analysis in biomedical text, which offers great potential for identifying semantic relationships between biomedical entities, terms, and terminologies [52,53]. Semantic analysis in the biomedical domain employs multiple NLP tasks, including WSD [54], clustering [55], ontology learning [56], information retrieval [33], text classification [57], question answering [39,41,42], text Summarization [58], topic detection [58], and many others. Extracting semantic similarity implies determining and quantifying the contextual relationship between concepts based on shared features.…”

Section: Semantic Enrichment Approachesmentioning

confidence: 99%

Survey on the Biomedical Text Summarization Techniques with an Emphasis on Databases, Techniques, Semantic Approaches, Classification Techniques, and Similarity Measures

et al. 2023

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Biomedical text summarization (BTS) is proving to be an emerging area of work and research with the need for sustainable healthcare applications such as evidence-based medicine practice (EBM) and telemedicine which help effectively support healthcare needs of the society. However, with the rapid growth in the biomedical literature and the diversities in its structure and resources, it is becoming challenging to carry out effective text summarization for better insights. The goal of this work is to conduct a comprehensive systematic literature review of significant and high-impact literary work in BTS with a deep understanding of its major artifacts such as databases, semantic similarity measures, and semantic enrichment approaches. In the systematic literature review conducted, we applied search filters to find high-impact literature in the biomedical text summarization domain from IEEE, SCOPUS, Elsevier, EBSCO, and PubMed databases. The systematic literature review (SLR) yielded 81 works; those were analyzed for qualitative study. The in-depth study of the literature shows the relevance and efficacy of the deep learning (DL) approach, context-aware feature extraction techniques, and their relevance in BTS. Biomedical question answering (BQA) system is one of the most popular applications of text summarizations for building self-sufficient healthcare systems and are pointing to future research directions. The review culminates in realization of a proposed framework for the BQA system MEDIQA with design of better heuristics for content screening, document screening, and relevance ranking. The presented framework provides an evidence-based biomedical question answering model and text summarizer that can lead to real-time evidence-based clinical support system to healthcare practitioners.

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Section: Semantic Enrichment Approachesmentioning

confidence: 99%

Survey on the Biomedical Text Summarization Techniques with an Emphasis on Databases, Techniques, Semantic Approaches, Classification Techniques, and Similarity Measures

et al. 2023

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“…The selection of papers is the result of a comprehensive literature search: section editors have pre-selected 15 papers [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] after a complex query from PubMed retrieving more than 1,000 articles, then 100 articles after a first selection based on title and abstract of these articles. Five reviewers reviewed the pre-selected papers to select the best four final papers (see Table 1) [1][2][3][4].…”

Section: About the Paper Selectionmentioning

confidence: 99%

“…These authors proposed a unifying framework to improve the understanding of these semantic measures. Gøeg et al [9] has evaluated Lin similarity estimates and Sokal and Sneath similarity with two aggregation techniques to cluster clinical models from electronic health records based on SNOMED-CT.…”

Section: Nomenclature Of Medicine-clinical Terms (Snomed-ct)mentioning

confidence: 99%

Knowledge Representation and Management. From Ontology to Annotation

Darmoni¹,

Charlet²

2015

Yearb Med Inform

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SummaryObjective: To summarize the best papers in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive review of medical informatics literature was performed to select some of the most interesting papers of KRM published in 2014. Results: Four articles were selected, two focused on annotation and information retrieval using an ontology. The two others focused mainly on ontologies, one dealing with the usage of a temporal ontology in order to analyze the content of narrative document, one describing a methodology for building multilingual ontologies. Conclusion: Semantic models began to show their efficiency, coupled with annotation tools.

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“…Following the similarity computations, templates were clustered hierarchically using dendrogams and this allowed a better overview of those templates and a starting point for additional analysis. The best-match-average showed a better performance than its counterpart and could be used for comparing templates along with SNOMED CT (Gøeg et al, 2015).…”

Section: Medical Data Clusteringmentioning

confidence: 99%

“…Another interesting study was conducted by Gøeg, Cornet and Andersen (2015) to cluster clinical models from local EHRs using semantic similarity. The purpose is to compare local templates, which are mostly not standardized and could make semantic interoperability more difficult.…”

Section: Medical Data Clusteringmentioning

confidence: 99%

Unsupervised Feature-Based Visualization Tool Applied on Medical Data. (c2020)

Moufarrej¹

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In a time when data is experiencing a remarkable growth in different fields, extracting and understanding the correlations between different data features is becoming increasingly important in many application areas, ranging over business, demographics, politics, and medicine, among others. The proper exploitation of such data offers great challenges in terms of data analysis and visualization, in order to allow effective and efficient decision-making. The problem is further aggravated on the Web where the data is often loosely structured and multi-featured. In this context, interactive data visualization has been recently suggested as a promising solution to facilitate data analysis and help in unveiling patterns, trends, and anomalies in the data. In this research, we present a new unsupervised feature-based tool for data visualization titled “mirrored dendrograms”. Our tool accepts as input semi-structured and multi-featured data, and allows the user to select the target features to be visualized and mapped against each other, as well as their relative impacts (weights) on the visualization process. It then invokes a hierarchical clustering process to cluster the data following the user-chosen features, and produces a dendrogram structure for each combination of target features. The dendrograms are then mirrored against each other by mapping the internal nodes of one dendrogram against the other dendrogram’s nodes, in order to highlight their structure correlation. Different from existing solutions like parallel coordinates and heatmap dendrograms, our work offers three main contributions: (i) connecting the dendrograms through their internal nodes (instead of connecting their leaf nodes like with heatmap dendrograms and tangelgrams) providing a visual representation of the relationships between the data structures, (ii) allowing to zoom-in and out the data to show their relationships at different granularity levels (compared with existing static solutions which do not allow varying granularities), and (iii) identifying the best zooming level between two dendrograms highlighting the maximum correlation (similarity) with the minimal amount of details (granularity) presented to the user (based on the intuition that users wish to acquire the most value out of the data, while viewing the least amount of data, i.e., with the least amount of effort). We have conducted a preliminary evaluation of our solution, using a sample dataset of 197 Electronic Health Records (EHRs) obtained from a private medical clinic, where all EHRs were vetted by a medical doctor from LAU Rizk hospital. The study was focused on the migraine headache disorder, where multiple patient data samples were mapped and visualized against each other. A number of 19 testers volunteered to participate in an online survey to help review and evaluate the data visualizations produced by our new tool, compared with existing solutions. Initial results are promising and highlight the potential our tool.

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Clustering clinical models from local electronic health records based on semantic similarity

Cited by 20 publications

References 28 publications

Survey on the Biomedical Text Summarization Techniques with an Emphasis on Databases, Techniques, Semantic Approaches, Classification Techniques, and Similarity Measures

Survey on the Biomedical Text Summarization Techniques with an Emphasis on Databases, Techniques, Semantic Approaches, Classification Techniques, and Similarity Measures

Knowledge Representation and Management. From Ontology to Annotation

Unsupervised Feature-Based Visualization Tool Applied on Medical Data. (c2020)

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