Representing oncology in datasets: Standard or custom biomedical terminology?

Schulz, Stefan; Daumke, Philipp; Romacker, Martin; López-García, Pablo

doi:10.1016/j.imu.2019.100186

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…Term identification is crucial for many tasks of automated biomedical text processing of biomedical [ 7 – 14 ], with CVs as fundamental resources [ 15 ]. We have proposed and validated a method not only to harvest terminology from texts but to classify texts by clinical specialties.…”

Section: Discussionmentioning

confidence: 99%

“…Most of these studies rely on controlled vocabularies (CVs) in different flavors, known as dictionaries, lexicons, terminologies and ontologies[ 15 ]. They are curated by experts and public bodies and are mostly tailored to specific purposes like disease or adverse event reporting, annotation of health records for billing, data collection for clinical research, and literature indexing.…”

Section: Background and Contributionsmentioning

confidence: 99%

“…They are curated by experts and public bodies and are mostly tailored to specific purposes like disease or adverse event reporting, annotation of health records for billing, data collection for clinical research, and literature indexing. They exhibit large differences in scope, granularity and underlying formalism, ranging from term lists, over informal thesauri, e.g., medical subject headings (MeSH) [ 16 ], single-hierarchy classification systems such as ICD-10 [ 17 ] and formal ontologies such as SNOMED CT [ 15 , 18 ].…”

Section: Background and Contributionsmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Background and Contributionsmentioning

confidence: 99%

Section: Background and Contributionsmentioning

confidence: 99%

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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

Self Cite

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“…The observation that clinical narratives are often characterized by complete freedom in text design, forms a contrast with the enormous amount of effort invested in vocabulary normalization over decades ( 16 ). To name just a few, ICD-10 ( 17 ) is a worldwide standard for encoding medical conditions.…”

Section: The Perspective Of Human Languagementioning

confidence: 99%

Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems

et al. 2023

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This paper provides an overview of current linguistic and ontological challenges which have to be met in order to provide full support to the transformation of health ecosystems in order to meet precision medicine (5 PM) standards. It highlights both standardization and interoperability aspects regarding formal, controlled representations of clinical and research data, requirements for smart support to produce and encode content in a way that humans and machines can understand and process it. Starting from the current text-centered communication practices in healthcare and biomedical research, it addresses the state of the art in information extraction using natural language processing (NLP). An important aspect of the language-centered perspective of managing health data is the integration of heterogeneous data sources, employing different natural languages and different terminologies. This is where biomedical ontologies, in the sense of formal, interchangeable representations of types of domain entities come into play. The paper discusses the state of the art of biomedical ontologies, addresses their importance for standardization and interoperability and sheds light to current misconceptions and shortcomings. Finally, the paper points out next steps and possible synergies of both the field of NLP and the area of Applied Ontology and Semantic Web to foster data interoperability for 5 PM.

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Kommunikationsfähigkeit und Interoperabilität von Gesundheitsdaten in einem vernetzten Gesundheitssystem

Daumke,

Haverkamp,

Heckmann

et al. 2024

Health Data Management

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Representing oncology in datasets: Standard or custom biomedical terminology?

Cited by 5 publications

References 20 publications

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

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

Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems

Kommunikationsfähigkeit und Interoperabilität von Gesundheitsdaten in einem vernetzten Gesundheitssystem

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