ReviewYearbook of medical informatics2018
Advancing the State of the Art in Clinical Natural Language Processing through Shared Tasks.
Review in Yearbook of medical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.BMC medical informatics and decision making · 2022Pooled it
- Temporal Annotation of German Clinical Language in Real and Synthetic Clinical Documents: Corpus Development and Baseline Tagger Validation Study.Journal of medical Internet research · 2026Article
- An analysis of entity normalization evaluation biases in specialized domains.BMC bioinformatics · 2023Article
- Spontaneously generated online patient experience data - how and why is it being used in health research: an umbrella scoping review.BMC medical research methodology · 2022Article
- Assessing the Performance of Clinical Natural Language Processing Systems: Development of an Evaluation Methodology.JMIR medical informatics · 2021Article
- A natural language processing approach for identifying temporal disease onset information from mental healthcare text.Scientific reports · 2021Article
- Medical Information Extraction in the Age of Deep Learning.Yearbook of medical informatics · 2020Review
- 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- AI in Health: State of the Art, Challenges, and Future Directions.Yearbook of medical informatics · 2019Review
- Expanding the Diversity of Texts and Applications: Findings from the Section on Clinical Natural Language Processing of the International Medical Informatics Association Yearbook.Yearbook of medical informatics · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
objectivesTo review the latest scientific challenges organized in clinical Natural Language Processing (NLP) by highlighting the tasks, the most effective methodologies used, the data, and the sharing strategies.
methodsWe harvested the literature by using Google Scholar and PubMed Central to retrieve all shared tasks organized since 2015 on clinical NLP problems on English data.
resultsWe surveyed 17 shared tasks. We grouped the data into four types (synthetic, drug labels, social data, and clinical data) which are correlated with size and sensitivity. We found named entity recognition and classification to be the most common tasks. Most of the methods used to tackle the shared tasks have been data-driven. There is homogeneity in the methods used to tackle the named entity recognition tasks, while more diverse solutions are investigated for relation extraction, multi-class classification, and information retrieval problems.
conclusionsThere is a clear trend in using data-driven methods to tackle problems in clinical NLP. The availability of more and varied data from different institutions will undoubtedly lead to bigger advances in the field, for the benefit of healthcare as a whole.
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.