ReviewYearbook of medical informatics2015
Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis.
Review in Yearbook of medical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed.
- [Implementation of artificial intelligence (AI) in healthcare: historical development, current technologies and challenges].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Review
- Identification of Child Survivors of Sex Trafficking From Electronic Health Records: An Artificial Intelligence Guided Approach.Child maltreatment · 2024Article
- Developing a Framework to Infer Opioid Use Disorder Severity From Clinical Notes to Inform Natural Language Processing Methods: Characterization Study.JMIR mental health · 2024Article
- A SNOMED CT Mapping Guideline for the Local Terms Used to Document Clinical Findings and Procedures in Electronic Medical Records in South Korea: Methodological Study.JMIR medical informatics · 2023Article
- Identifying Datasets for Cross-Study Analysis in dbGaP using PhenX.Scientific data · 2022Article
- Exploring Sentiment and Care Management of Hospitalized Patients During the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records: Descriptive Study.JMIR medical informatics · 2022Article
- Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques.International journal of environmental research and public health · 2022Article
- Evaluation of Natural Language Processing for the Identification of Crohn Disease-Related Variables in Spanish Electronic Health Records: A Validation Study for the PREMONITION-CD Project.JMIR medical informatics · 2022Article
- Detecting Alzheimer's Disease Using Natural Language Processing of Referential Communication Task Transcripts.Journal of Alzheimer's disease : JAD · 2022Article
- Healthcare professionals' perceptions on medication communication challenges and solutions - text mining and manual content analysis - cross-sectional study.BMC health services research · 2021Article
- Health Natural Language Processing: Methodology Development and Applications.JMIR medical informatics · 2021Article
- Assessing the Performance of Clinical Natural Language Processing Systems: Development of an Evaluation Methodology.JMIR medical informatics · 2021Article
- Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing.Methods of information in medicine · 2020Article
- Medical Information Extraction in the Age of Deep Learning.Yearbook of medical informatics · 2020Review
- Review
- Should free-text data in electronic medical records be shared for research? A citizens' jury study in the UK.Journal of medical ethics · 2020Article
- Article
- Observational
- Analysis of Inter-Domain and Cross-Domain Drug Review Polarity Classification.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Natural language processing for disease phenotyping in UK primary care records for research: a pilot study in myocardial infarction and death.Journal of biomedical semantics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
objectivesWe present a review of recent advances in clinical Natural Language Processing (NLP), with a focus on semantic analysis and key subtasks that support such analysis.
methodsWe conducted a literature review of clinical NLP research from 2008 to 2014, emphasizing recent publications (2012-2014), based on PubMed and ACL proceedings as well as relevant referenced publications from the included papers.
resultsSignificant articles published within this time-span were included and are discussed from the perspective of semantic analysis. Three key clinical NLP subtasks that enable such analysis were identified: 1) developing more efficient methods for corpus creation (annotation and de-identification), 2) generating building blocks for extracting meaning (morphological, syntactic, and semantic subtasks), and 3) leveraging NLP for clinical utility (NLP applications and infrastructure for clinical use cases). Finally, we provide a reflection upon most recent developments and potential areas of future NLP development and applications.
conclusionsThere has been an increase of advances within key NLP subtasks that support semantic analysis. Performance of NLP semantic analysis is, in many cases, close to that of agreement between humans. The creation and release of corpora annotated with complex semantic information models has greatly supported the development of new tools and approaches. Research on non-English languages is continuously growing. NLP methods have sometimes been successfully employed in real-world clinical tasks. However, there is still a gap between the development of advanced resources and their utilization in clinical settings. A plethora of new clinical use cases are emerging due to established health care initiatives and additional patient-generated sources through the extensive use of social media and other devices.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.