scholarly journals Clinical Natural Language Processing in 2015: Leveraging the Variety of Texts of Clinical Interest

2016 ◽  
Vol 25 (01) ◽  
pp. 234-239 ◽  
Author(s):  
P. Zweigenbaum ◽  
A. Névéol ◽  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Clinical Information ◽  
Bibliographic Databases ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Clinical Interest ◽  
Clinical Natural Language Processing ◽  
Selection Of

Summary Objective: To summarize recent research and present a selection of the best papers published in 2015 in the field of clinical Natural Language Processing (NLP). Method: A systematic review of the literature was performed by the two section editors of the IMIA Yearbook NLP section by searching bibliographic databases with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Section editors first selected a shortlist of candidate best papers that were then peer-reviewed by independent external reviewers. Results: The clinical NLP best paper selection shows that clinical NLP is making use of a variety of texts of clinical interest to contribute to the analysis of clinical information and the building of a body of clinical knowledge. The full review process highlighted five papers analyzing patient-authored texts or seeking to connect and aggregate multiple sources of information. They provide a contribution to the development of methods, resources, applications, and sometimes a combination of these aspects. Conclusions: The field of clinical NLP continues to thrive through the contributions of both NLP researchers and healthcare professionals interested in applying NLP techniques to impact clinical practice. Foundational progress in the field makes it possible to leverage a larger variety of texts of clinical interest for healthcare purposes.

scholarly journals Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing

2017 ◽  
Vol 26 (01) ◽  
pp. 228-234 ◽  
Author(s):  
A. Névéol ◽  
P. Zweigenbaum
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Bibliographic Databases ◽  
Coreference Resolution ◽  
Gradable Adjectives ◽  
Research Results ◽  
Making Sense ◽  
Textual Data ◽  
Clinical Natural Language Processing

Summary Objectives: To summarize recent research and present a selection of the best papers published in 2016 in the field of clinical Natural Language Processing (NLP). Method: A survey of the literature was performed by the two section editors of the IMIA Yearbook NLP section. Bibliographic databases were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Papers were automatically ranked and then manually reviewed based on titles and abstracts. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers. Results: The five clinical NLP best papers provide a contribution that ranges from emerging original foundational methods to transitioning solid established research results to a practical clinical setting. They offer a framework for abbreviation disambiguation and coreference resolution, a classification method to identify clinically useful sentences, an analysis of counseling conversations to improve support to patients with mental disorder and grounding of gradable adjectives. Conclusions: Clinical NLP continued to thrive in 2016, with an increasing number of contributions towards applications compared to fundamental methods. Fundamental work addresses increasingly complex problems such as lexical semantics, coreference resolution, and discourse analysis. Research results translate into freely available tools, mainly for English.

scholarly journals Expanding the Diversity of Texts and Applications: Findings from the Section on Clinical Natural Language Processing of the International Medical Informatics Association Yearbook

2018 ◽  
Vol 27 (01) ◽  
pp. 193-198 ◽  
Author(s):  
Aurélie Névéol ◽  
Pierre Zweigenbaum ◽  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Medical Informatics ◽  
Computational Linguistics ◽  
Language Processing ◽  
English Language ◽  
International Medical ◽  
Bibliographic Databases ◽  
Research Results ◽  
Clinical Natural Language Processing

Objectives: To summarize recent research and present a selection of the best papers published in 2017 in the field of clinical Natural Language Processing (NLP). Methods: A survey of the literature was performed by the two editors of the NLP section of the International Medical Informatics Association (IMIA) Yearbook. Bibliographic databases PubMed and Association of Computational Linguistics (ACL) Anthology were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. A total of 709 papers were automatically ranked and then manually reviewed based on title and abstract. A shortlist of 15 candidate best papers was selected by the section editors and peer-reviewed by independent external reviewers to come to the three best clinical NLP papers for 2017. Results: Clinical NLP best papers provide a contribution that ranges from methodological studies to the application of research results to practical clinical settings. They draw from text genres as diverse as clinical narratives across hospitals and languages or social media. Conclusions: Clinical NLP continued to thrive in 2017, with an increasing number of contributions towards applications compared to fundamental methods. Methodological work explores deep learning and system adaptation across language variants. Research results continue to translate into freely available tools and corpora, mainly for the English language.

scholarly journals Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing

2021 ◽  
Vol 30 (01) ◽  
pp. 257-263
Author(s):  
Natalia Grabar ◽  
Cyril Grouin ◽  
Keyword(s):  
Social Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Scientific Literature ◽  
The Past ◽  
Clinical Natural Language Processing ◽  
Selection Of

Summary Objectives: To analyze the content of publications within the medical NLP domain in 2020. Methods: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues. Results: Three best papers have been selected in 2020. We also propose an analysis of the content of the NLP publications in 2020, all topics included. Conclusion: The two main issues addressed in 2020 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as diversification of languages processed and use of information from social networks

scholarly journals Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing

2017 ◽  
Vol 26 (01) ◽  
pp. 228-233
Author(s):  
A. Névéol ◽  
P. Zweigenbaum
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Bibliographic Databases ◽  
Coreference Resolution ◽  
Gradable Adjectives ◽  
Research Results ◽  
Making Sense ◽  
Textual Data ◽  
Clinical Natural Language Processing

Summary Objectives: To summarize recent research and present a selection of the best papers published in 2016 in the field of clinical Natural Language Processing (NLP). Method: A survey of the literature was performed by the two section editors of the IMIA Yearbook NLP section. Bibliographic databases were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Papers were automatically ranked and then manually reviewed based on titles and abstracts. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers. Results: The five clinical NLP best papers provide a contribution that ranges from emerging original foundational methods to transitioning solid established research results to a practical clinical setting. They offer a framework for abbreviation disambiguation and coreference resolution, a classification method to identify clinically useful sentences, an analysis of counseling conversations to improve support to patients with mental disorder and grounding of gradable adjectives. Conclusions: Clinical NLP continued to thrive in 2016, with an increasing number of contributions towards applications compared to fundamental methods. Fundamental work addresses increasingly complex problems such as lexical semantics, coreference resolution, and discourse analysis. Research results translate into freely available tools, mainly for English.

scholarly journals Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare

2015 ◽  
Vol 24 (01) ◽  
pp. 194-198 ◽  
Author(s):  
A. Névéol ◽  
P. Zweigenbaum ◽  
Keyword(s):  
Natural Language Processing ◽  
Clinical Outcome ◽  
Natural Language ◽  
Language Processing ◽  
Temporal Analysis ◽  
Bibliographic Databases ◽  
Specific Information ◽  
Word Sense ◽  
Practical Applications ◽  
Clinical Natural Language Processing

Summary Objective: To summarize recent research and present a selection of the best papers published in 2014 in the field of clinical Natural Language Processing (NLP).Method: A systematic review of the literature was performed by the two section editors of the IMIA Yearbook NLP section by searching bibliographic databases with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers. Results: The clinical NLP best paper selection shows that the field is tackling text analysis methods of increasing depth. The full review process highlighted five papers addressing foundational methods in clinical NLP using clinically relevant texts from online forums or encyclopedias, clinical texts from Electronic Health Records, and included studies specifically aiming at a practical clinical outcome. The increased access to clinical data that was made possible with the recent progress of de-identification paved the way for the scientific community to address complex NLP problems such as word sense disambiguation, negation, temporal analysis and specific information nugget extraction. These advances in turn allowed for efficient application of NLP to clinical problems such as cancer patient triage. Another line of research investigates online clinically relevant texts and brings interesting insight on communication strategies to convey health-related information. Conclusions: The field of clinical NLP is thriving through the contributions of both NLP researchers and healthcare professionals interested in applying NLP techniques for concrete healthcare purposes. Clinical NLP is becoming mature for practical applications with a significant clinical impact.

scholarly journals A deep database of medical abbreviations and acronyms for natural language processing

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Lisa Grossman Liu ◽  
Raymond H. Grossman ◽  
Elliot G. Mitchell ◽  
Chunhua Weng ◽  
Karthik Natarajan ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
American English ◽  
Substantial Improvement ◽  
Future Application ◽  
Multiple Sources ◽  
High Coverage ◽  
Clinical Text ◽  
Automated Quality Control

AbstractThe recognition, disambiguation, and expansion of medical abbreviations and acronyms is of upmost importance to prevent medically-dangerous misinterpretation in natural language processing. To support recognition, disambiguation, and expansion, we present the Medical Abbreviation and Acronym Meta-Inventory, a deep database of medical abbreviations. A systematic harmonization of eight source inventories across multiple healthcare specialties and settings identified 104,057 abbreviations with 170,426 corresponding senses. Automated cross-mapping of synonymous records using state-of-the-art machine learning reduced redundancy, which simplifies future application. Additional features include semi-automated quality control to remove errors. The Meta-Inventory demonstrated high completeness or coverage of abbreviations and senses in new clinical text, a substantial improvement over the next largest repository (6–14% increase in abbreviation coverage; 28–52% increase in sense coverage). To our knowledge, the Meta-Inventory is the most complete compilation of medical abbreviations and acronyms in American English to-date. The multiple sources and high coverage support application in varied specialties and settings. This allows for cross-institutional natural language processing, which previous inventories did not support. The Meta-Inventory is available at https://bit.ly/github-clinical-abbreviations.

scholarly journals Cohort Selection for Construction of a Clinical Natural Language Processing Corpus

2021 ◽  
pp. 100024
Author(s):  
Naga Lalitha Valli ALLA ◽  
Aipeng CHEN ◽  
Sean BATONGBACAL ◽  
Chandini NEKKANTTI ◽  
Hong-Jie Dai ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Cohort Selection ◽  
Selection For ◽  
Clinical Natural Language Processing

scholarly journals Toward a clinical text encoder: pretraining for clinical natural language processing with applications to substance misuse

2019 ◽  
Vol 26 (11) ◽  
pp. 1272-1278 ◽  
Author(s):  
Dmitriy Dligach ◽  
Majid Afshar ◽  
Timothy Miller
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Substance Misuse ◽  
Research Direction ◽  
Clinical Text ◽  
Universal Properties ◽  
Classification Tasks ◽  
Performance Gains ◽  
Clinical Natural Language Processing

Abstract Objective Our objective is to develop algorithms for encoding clinical text into representations that can be used for a variety of phenotyping tasks. Materials and Methods Obtaining large datasets to take advantage of highly expressive deep learning methods is difficult in clinical natural language processing (NLP). We address this difficulty by pretraining a clinical text encoder on billing code data, which is typically available in abundance. We explore several neural encoder architectures and deploy the text representations obtained from these encoders in the context of clinical text classification tasks. While our ultimate goal is learning a universal clinical text encoder, we also experiment with training a phenotype-specific encoder. A universal encoder would be more practical, but a phenotype-specific encoder could perform better for a specific task. Results We successfully train several clinical text encoders, establish a new state-of-the-art on comorbidity data, and observe good performance gains on substance misuse data. Discussion We find that pretraining using billing codes is a promising research direction. The representations generated by this type of pretraining have universal properties, as they are highly beneficial for many phenotyping tasks. Phenotype-specific pretraining is a viable route for trading the generality of the pretrained encoder for better performance on a specific phenotyping task. Conclusions We successfully applied our approach to many phenotyping tasks. We conclude by discussing potential limitations of our approach.

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