MedSearch: A Retrieval System for Medical Information Based on Semantic Similarity

Hliaoutakis, Angelos; Varelas, Giannis; Petrakis, Euripides G. M.; Milios, Evangelos

doi:10.1007/11863878_56

Cited by 18 publications

(10 citation statements)

References 1 publication

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“…Many methodologies have been used to properly extract and classify clinical terms. In [7] the authors have proposed a retrieval system (MedSearch) for medical literature based on semantic similarity retrieval model (SSRM) which is capable for associating documents containing semantically similar (but not necessarily lexically similar) terms. SSRM suggests discovering semantically similar terms in documents and queries using ontologies and by associating such terms using semantic similarity methods.…”

Section: A Semantic Analysis and Knowledge Extractionmentioning

confidence: 99%

Semantic-based approach for predicting venous thromboembolism using Kohonen self organized map neural network

Sabra

Mahmood

Alobaidi

2016

2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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This paper presents a novel semantic-based approach to analyze clinical documentation in order to predict the first occurrence of symptomless unprovoked venous thromboembolism (VTE) in patients. The goal of this work is the attempt to save lives in cases of symptomless unprovoked VTEs that could be fatal from first occurrence. Using the Unified Medical Language System (UMLS) and MetaMap API, our semantic approach extracts hidden factors from unstructured text that might be very critical in the diagnosis or identification of potential risks of having a VTE. We use Kohonen self-organizing map to predict a patient's potential to develop VTE based on the similarity between the identified hidden risk factors in the clinical notes section from their medical records and the clinical notes used in the learning phase. Our system achieved 80% accuracy with 50% precision in prediction.

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Section: A Semantic Analysis and Knowledge Extractionmentioning

confidence: 99%

Semantic-based approach for predicting venous thromboembolism using Kohonen self organized map neural network

Sabra

Mahmood

Alobaidi

2016

2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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“…In this paper, we use semantic similarity measure proposed by Li et al [11], which significantly outperformed traditional similarity measures [12].…”

Section: Semantic Field Constructionmentioning

confidence: 99%

A Novel Ontology Analysis Tool

Wang¹,

Xia²,

Niu³

2014

Appl. Math. Inf. Sci.

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Abstract:With the development of semantic web, more and more ontologies are developed for various purposes. In order to evaluate these ontologies, there have emerged numerous ontology analysis approaches. This paper introduces the classical field theory to ontology area, and puts forward a semantic field model to analyze ontology characteristics from the perspective of concept interaction and hierarchical clustering. In semantic field, the equipotential lines present a natural nesting structure. The equipotential lines' distribution corresponds with the semantics' distribution resulting from the interaction and association of ontology concepts. Based on equipotential line distribution, we can analyze concept hierarchical clustering characteristics. Experiments showed that the semantic field does well in ontology concept hierarchical clustering analysis. The semantic field model has good scalability, and clustering execution time follows an approximate linear relationship with concept scale.

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“…Only Nandi & Bernstein have proposed a technique which was based on logs from virtual shops for computing similarity between products [26]. However, a number of works have addressed the semantic similarity measurement [16], [28], [30], [34], [35], and the use of WI techniques for solving computational problems [19], [36], [37] separately.…”

Section: Related Workmentioning

confidence: 99%

Semantic similarity measurement using historical google search patterns

Martinez‐Gil

Aldana‐Montes

2013

Inf Syst Front

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Computing the similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is a key challenge in the information integration field. The problem is that techniques for textual semantic similarity measurement often fail to deal with words not covered by synonym dictionaries. In this paper, we try to solve this problem by determining the semantic similarity for terms using the knowledge inherent in the search history logs from the Google search engine. To do that, we have designed and evaluated four algorithmic methods for measuring the semantic similarity between terms using their associated history search patterns. These algorithmic methods are: a) frequent co-occurrence of terms in search patterns, b) computation of the relationship between search patterns, c) outlier coincidence on search patterns, and d) forecasting comparisons. We have shown experimentally that some of these methods correlate well with respect to human judgment when evaluating general purpose benchmark datasets, and significantly outperform existing methods when evaluating datasets containing terms that do not usually appear in dictionaries.

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MedSearch: A Retrieval System for Medical Information Based on Semantic Similarity

Cited by 18 publications

References 1 publication

Semantic-based approach for predicting venous thromboembolism using Kohonen self organized map neural network

Semantic-based approach for predicting venous thromboembolism using Kohonen self organized map neural network

A Novel Ontology Analysis Tool

Semantic similarity measurement using historical google search patterns

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