How knowledge drives understanding—matching medical ontologies with the needs of medical language processing

Hahn, Udo; Romacker, Martin; Schulz, Stefan

doi:10.1016/s0933-3657(98)00044-x

Cited by 37 publications

(9 citation statements)

References 22 publications

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“…Clinical pragmatics -getting the clinical dialogue right so that it fits into daily tasks and practice. Clinical computational linguistics is a relatively well developed field with significant successes such as the Linguistic String Project [20], the work of Scherrer, Baud, Rassinoux and others in Geneva [21][22][23][24], and others [25,26].…”

Section: The Importance Of Clinical Pragmaticsmentioning

confidence: 99%

Clinical Terminology: Why Is it so Hard?

Rector¹

1999

Methods Inf Med

152

108

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Despite years of work, no re-usable clinical terminology has yet been demonstrated in widespread use. This paper puts forward ten reasons why developing such terminologies is hard. All stem from underestimating the change entailed in using terminology in software for ‘patient centred’ systems rather than for its traditional functions of statistical and financial reporting. Firstly, the increase in scale and complexity are enormous. Secondly, the resulting scale exceeds what can be managed manually with the rigour required by software, but building appropriate rigorous representations on the necessary scale is, in itself, a hard problem. Thirdly, ‘clinical pragmatics’ – practical data entry, presentation and retrieval for clinical tasks – must be taken into account, so that the intrinsic differences between the needs of users and the needs of software are addressed. This implies that validation of clinical terminologies must include validation in use as implemented in software.

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Section: The Importance Of Clinical Pragmaticsmentioning

confidence: 99%

Clinical Terminology: Why Is it so Hard?

Rector¹

1999

Methods Inf Med

152

108

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“…One challenge of designing the mapping system is the diverse forms of an anatomical entities. Named entities related to body parts, organs, and their subparts are defined as anatomical entities [4]. In this paper, we use the phrase “anatomical related entities” to denote named entities and their related medical terms, including problems, medical tests, and treatments.…”

Section: Introductionmentioning

confidence: 99%

Mapping anatomical related entities to human body parts based on wikipedia in discharge summaries

et al. 2019

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* Background Consisting of dictated free-text documents such as discharge summaries, medical narratives are widely used in medical natural language processing. Relationships between anatomical entities and human body parts are crucial for building medical text mining applications. To achieve this, we establish a mapping system consisting of a Wikipedia-based scoring algorithm and a named entity normalization method (NEN). The mapping system makes full use of information available on Wikipedia, which is a comprehensive Internet medical knowledge base. We also built a new ontology, Tree of Human Body Parts (THBP), from core anatomical parts by referring to anatomical experts and Unified Medical Language Systems (UMLS) to make the mapping system efficacious for clinical treatments. * Result The gold standard is derived from 50 discharge summaries from our previous work, in which 2,224 anatomical entities are included. The F1-measure of the baseline system is 70.20%, while our algorithm based on Wikipedia achieves 86.67% with the assistance of NEN. * Conclusions We construct a framework to map anatomical entities to THBP ontology using normalization and a scoring algorithm based on Wikipedia. The proposed framework is proven to be much more effective and efficient than the main baseline system.

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“…However, the existence of such terminological resources does not guarantee their utility for NLP. In particular, we have two core requirements for lexical resources for NLP in addition to the basic enumeration of important domain terms: representation of morphosyntactic information about those terms, specifically part of speech information and inflectional patterns to support parsing and lemma assignment, and representation of semantic information indicating general categorical information about terms and significant relations between terms, to support text understanding and inference (Hahn et al, 1999). Biomedical vocabularies by and large commonly leave out morphosyntactic information, and where they address semantic considerations, they often do so in an unprincipled manner, e.g.…”

Section: Introductionmentioning

confidence: 99%

Towards a semantic lexicon for biological language processing

Verspoor

2005

Comp Funct Genom

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This paper explores the use of the resources in the National Library of Medicine's Unified Medical Language System (UMLS) for the construction of a lexicon useful for processing texts in the field of molecular biology. A lexicon is constructed from overlapping terms in the UMLS SPECIALIST lexicon and the UMLS Metathesaurus to obtain both morphosyntactic and semantic information for terms, and the coverage of a domain corpus is assessed. Over 77% of tokens in the domain corpus are found in the constructed lexicon, validating the lexicon's coverage of the most frequent terms in the domain and indicating that the constructed lexicon is potentially an important resource for biological text processing.

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How knowledge drives understanding—matching medical ontologies with the needs of medical language processing

Cited by 37 publications

References 22 publications

Clinical Terminology: Why Is it so Hard?

Clinical Terminology: Why Is it so Hard?

Mapping anatomical related entities to human body parts based on wikipedia in discharge summaries

Towards a semantic lexicon for biological language processing

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