An Integrated Approach to Biomedical Term Identification Systems

López-Úbeda, Pilar; Díaz–Galiano, Manuel Carlos; Montejo-Ráez, Arturo; Martín–Valdivia, María Teresa; Ureña‐López, L. Alfonso

doi:10.3390/app10051726

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…An Integrated Approach to Biomedical Term Identification Systems [11] combines several sources of information and knowledge bases to provide biomedical term identification systems with modular architecture, which includes medical term identification, retrieval of literature and ontology browsing by applying several NLP technologies. The similarity with our system is in combining several terminological and lexical resources, as well as the use of various NLP techniques, while the difference is that their system generates a conceptual graph that semantically relates all the terms found in the text, which would be our plan for future research.…”

Section: Discussionmentioning

confidence: 99%

“…López-Úbeda et al [11] present another interesting approach, which also combines different NLP techniques to develop a system for identification of biomedical terms in textual documents written in Spanish. The approach was applied for recognizing biomedical entities in various types of texts, including different knowledge resources (MedLine Encyclopedia, International Classification of Diseases, Unified Medical Language System, etc.).…”

Section: Introductionmentioning

confidence: 99%

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A Data Driven Approach for Raw Material Terminology

et al. 2021

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The research presented in this paper aims at creating a bilingual (sr-en), easily searchable, hypertext, born-digital, corpus-based terminological database of raw material terminology for dictionary production. The approach is based on linking dictionaries related to the raw material domain, both digitally born and printed, into a lexicon structure, aligning terminology from different dictionaries as much as possible. This paper presents the main features of this approach, data used for compilation of the terminological database, the procedure by which it has been generated and a mobile application for its use. Available (terminological) resources will be presented—paper dictionaries and digital resources related to the raw material domain, as well as general lexica morphological dictionaries. Resource preparation started with dictionary (retro)digitisation and corpora enlargement, followed by adding new Serbian terms to general lexica dictionaries, as well as adding bilingual terms. Dictionary development is relying on corpus analysis, details of which are also presented. Usage examples, collocations and concordances play an important role in raw material terminology, and have also been included in this research. Some important related issues discussed are collocation extraction methods, the use of domain labels, lexical and semantic relations, definitions and subentries.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Data Driven Approach for Raw Material Terminology

et al. 2021

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“…Drug and chemical name recognition, which seeks to recognize these types of mentions in unstructured medical texts and classify them into pre-defined categories, is a fundamental task of medical information extraction and medical relation extraction systems [ 13 – 15 ], and is the key to linking entities with terminologies available in the biomedical domain such as SNOMED-CT [ 16 – 19 ].…”

Section: Related Workmentioning

confidence: 99%

Combining word embeddings to extract chemical and drug entities in biomedical literature

et al. 2021

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Background Natural language processing (NLP) and text mining technologies for the extraction and indexing of chemical and drug entities are key to improving the access and integration of information from unstructured data such as biomedical literature. Methods In this paper we evaluate two important tasks in NLP: the named entity recognition (NER) and Entity indexing using the SNOMED-CT terminology. For this purpose, we propose a combination of word embeddings in order to improve the results obtained in the PharmaCoNER challenge. Results For the NER task we present a neural network composed of BiLSTM with a CRF sequential layer where different word embeddings are combined as an input to the architecture. A hybrid method combining supervised and unsupervised models is used for the concept indexing task. In the supervised model, we use the training set to find previously trained concepts, and the unsupervised model is based on a 6-step architecture. This architecture uses a dictionary of synonyms and the Levenshtein distance to assign the correct SNOMED-CT code. Conclusion On the one hand, the combination of word embeddings helps to improve the recognition of chemicals and drugs in the biomedical literature. We achieved results of 91.41% for precision, 90.14% for recall, and 90.77% for F1-score using micro-averaging. On the other hand, our indexing system achieves a 92.67% F1-score, 92.44% for recall, and 92.91% for precision. With these results in a final ranking, we would be in the first position.

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“…Information extracted from clinical narratives has been used for a wide range of biomedical applications [ 1 – 4 ], and natural language processing (NLP) and machine learning (ML) techniques have evolved as important parts of clinical information extraction initiatives [ 5 ]. Information extraction supports a wide variety of clinical and research use cases, such as building disease-specific cohorts [ 5 ], processing and analyzing mentions of signs and symptoms [ 6 , 7 ], detecting and assessing adverse drug events and risks [ 8 – 10 ], extracting key information for reporting or quality assurance [ 11 – 14 ], among others.…”

Section: Background and Contributionsmentioning

confidence: 99%

Collecting specialty-related medical terms: Development and evaluation of a resource for Spanish

López-Úbeda

Pomares-Quimbaya

Díaz–Galiano

et al. 2021

BMC Med Inform Decis Mak

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Background Controlled vocabularies are fundamental resources for information extraction from clinical texts using natural language processing (NLP). Standard language resources available in the healthcare domain such as the UMLS metathesaurus or SNOMED CT are widely used for this purpose, but with limitations such as lexical ambiguity of clinical terms. However, most of them are unambiguous within text limited to a given clinical specialty. This is one rationale besides others to classify clinical text by the clinical specialty to which they belong. Results This paper addresses this limitation by proposing and applying a method that automatically extracts Spanish medical terms classified and weighted per sub-domain, using Spanish MEDLINE titles and abstracts as input. The hypothesis is biomedical NLP tasks benefit from collections of domain terms that are specific to clinical subdomains. We use PubMed queries that generate sub-domain specific corpora from Spanish titles and abstracts, from which token n-grams are collected and metrics of relevance, discriminatory power, and broadness per sub-domain are computed. The generated term set, called Spanish core vocabulary about clinical specialties (SCOVACLIS), was made available to the scientific community and used in a text classification problem obtaining improvements of 6 percentage points in the F-measure compared to the baseline using Multilayer Perceptron, thus demonstrating the hypothesis that a specialized term set improves NLP tasks. Conclusion The creation and validation of SCOVACLIS support the hypothesis that specific term sets reduce the level of ambiguity when compared to a specialty-independent and broad-scope vocabulary.

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An Integrated Approach to Biomedical Term Identification Systems

Cited by 6 publications

References 33 publications

A Data Driven Approach for Raw Material Terminology

A Data Driven Approach for Raw Material Terminology

Combining word embeddings to extract chemical and drug entities in biomedical literature

Collecting specialty-related medical terms: Development and evaluation of a resource for Spanish

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