Medical Query Expansion using UMLS

Kumar, K. Saravana; Deepa, K.

doi:10.17485/ijst/2016/v9i14/77644

Cited by 3 publications

(2 citation statements)

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“…The integration of semantic knowledge in the medical image retrieval domain has received great attention, such as [ 25 , 26 , 27 ]. Authors in [ 28 ] used UMLS meta-thesaurus in the medical domain to improve queries and converting words to medical terms. They integrated the semantics in the retrieval process to map the text into concepts using UMLS meta-thesaurus [ 29 ].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Medical Image Retrieval with UMLS-Integrated CNN-Based Text Indexing

Gasmi,

Ayadi,

Torjmen

2024

Diagnostics

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In recent years, Convolutional Neural Network (CNN) models have demonstrated notable advancements in various domains such as image classification and Natural Language Processing (NLP). Despite their success in image classification tasks, their potential impact on medical image retrieval, particularly in text-based medical image retrieval (TBMIR) tasks, has not yet been fully realized. This could be attributed to the complexity of the ranking process, as there is ambiguity in treating TBMIR as an image retrieval task rather than a traditional information retrieval or NLP task. To address this gap, our paper proposes a novel approach to re-ranking medical images using a Deep Matching Model (DMM) and Medical-Dependent Features (MDF). These features incorporate categorical attributes such as medical terminologies and imaging modalities. Specifically, our DMM aims to generate effective representations for query and image metadata using a personalized CNN, facilitating matching between these representations. By using MDF, a semantic similarity matrix based on Unified Medical Language System (UMLS) meta-thesaurus, and a set of personalized filters taking into account some ranking features, our deep matching model can effectively consider the TBMIR task as an image retrieval task, as previously mentioned. To evaluate our approach, we performed experiments on the medical ImageCLEF datasets from 2009 to 2012. The experimental results show that the proposed model significantly enhances image retrieval performance compared to the baseline and state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Enhancing Medical Image Retrieval with UMLS-Integrated CNN-Based Text Indexing

Gasmi,

Ayadi,

Torjmen

2024

Diagnostics

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“…SNOMED-CT (Systematized Nomenclature of Medicine-Clinical Terms) [ 9 ] is a rather comprehensive medical terminology, which uses a formally defined medical ontology as the backbone for concepts and terms. UMLS (Unified Medical Language System) [ 10 ] metathesaurus is a project initiated by US National Library of Medicine, aiming at mapping concepts in existing terminologies into a comprehensive metathesaurus ontology. The current UMLS version has integrated more than 200 existing terminologies.…”

Section: Related Workmentioning

confidence: 99%

A Domain-Specific Terminology for Retinopathy of Prematurity and Its Applications in Clinical Settings

Zhang

2018

Journal of Healthcare Engineering

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A terminology (or coding system) is a formal set of controlled vocabulary in a specific domain. With a well-defined terminology, each concept in the target domain is assigned with a unique code, which can be identified and processed across different medical systems in an unambiguous way. Though there are lots of well-known biomedical terminologies, there is currently no domain-specific terminology for ROP (retinopathy of prematurity). Based on a collection of historical ROP patients' data in the electronic medical record system, we extracted the most frequent terms in the domain and organized them into a hierarchical coding system—ROP Minimal Standard Terminology, which contains 62 core concepts in 4 categories. This terminology has been successfully used to provide highly structured and semantic-rich clinical data in several ROP-related applications.

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Document/query expansion based on selecting significant concepts for context based retrieval of medical images

Torjmen-Khemakhem

Gasmi

2019

Journal of Biomedical Informatics

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Medical Query Expansion using UMLS

Cited by 3 publications

References 0 publications

Enhancing Medical Image Retrieval with UMLS-Integrated CNN-Based Text Indexing

Enhancing Medical Image Retrieval with UMLS-Integrated CNN-Based Text Indexing

A Domain-Specific Terminology for Retinopathy of Prematurity and Its Applications in Clinical Settings

Document/query expansion based on selecting significant concepts for context based retrieval of medical images

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