Leveraging Sublanguage Features for the Semantic Categorization of Clinical Terms

Grön, Leonie; Bertels, Ann; Heylen, Kris

doi:10.18653/v1/w19-5022

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Publications, n (%) Data language [15-17,19-25,27-38,41-48,50,51,53-68,71,72,74,75,78-80,83-88,90-92,96,97,99-112,114,116,119-124,126-135, 137,140,142-149,151-154,156-159,161,162,165-176,179,181,184-187,189-192,194-196,198,200-208] 153 (78.9) English [39,49,52,73,76,77,81,89,93,94,113,115,118,125,136,138,139,155,163,164,177,178,182,183,188,193,197] 27 (13.9) French [18,26,69,95,117,150,160,180,199] 9 (4.6) German [40,65,82] 3 (1.5) Korean…”

Section: Referencesmentioning

confidence: 99%

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

Bazoge,

Morin,

Daille

et al. 2023

JMIR Med Inform

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Background In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible. Objective The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks. Methods This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English. Results We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%). Conclusions CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.

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Section: Referencesmentioning

confidence: 99%

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

Bazoge,

Morin,

Daille

et al. 2023

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

“…As methods for automated analysis of large-scale data sets have improved, more studies have investigated lexical and semantic characteristics, such as usage patterns of different verbs and semantic categories (Denecke, 2014), as well as more structural information such as document section patterns and syntactic features (Zeng et al, 2011;Temnikova et al, 2014). The use of terminologies to assess conceptual features of a sublanguage corpus was proposed by Walker andAmsler (1986), andDrouin (2004); Grön et al (2019) used sublanguage features to expand existing terminologies, but largescale characterization of concept usage in sublanguage has remained a challenging question.…”

Section: Related Workmentioning

confidence: 99%

“…Sublanguage analysis has played a pivotal role in natural language processing of health data, from highlighting the clear linguistic differences between biomedical literature and clinical text (Friedman et al, 2002) to supporting adaptation to multiple languages (Laippala et al, 2009). Recent studies of clinical sublanguage have extended sublanguage study to the document type level, in order to improve our understanding of the syntactic and lexical differences between highly distinct document types used in modern EHR systems (Feldman et al, 2016;Grön et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Writing habits and telltale neighbors: analyzing clinical concept usage patterns with sublanguage embeddings

Newman-Griffis

Fosler‐Lussier

2019

Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

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Natural language processing techniques are being applied to increasingly diverse types of electronic health records, and can benefit from in-depth understanding of the distinguishing characteristics of medical document types. We present a method for characterizing the usage patterns of clinical concepts among different document types, in order to capture semantic differences beyond the lexical level. By training concept embeddings on clinical documents of different types and measuring the differences in their nearest neighborhood structures, we are able to measure divergences in concept usage while correcting for noise in embedding learning. Experiments on the MIMIC-III corpus demonstrate that our approach captures clinically-relevant differences in concept usage and provides an intuitive way to explore semantic characteristics of clinical document collections.

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Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review (Preprint)

Bazoge,

Morin,

Daille

et al. 2022

Preprint

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BACKGROUND In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible. OBJECTIVE The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks. METHODS This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English. RESULTS We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%). CONCLUSIONS CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.

show abstract

Leveraging Sublanguage Features for the Semantic Categorization of Clinical Terms

Cited by 3 publications

References 18 publications

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

Writing habits and telltale neighbors: analyzing clinical concept usage patterns with sublanguage embeddings

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review (Preprint)

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