ReviewYearbook of medical informatics2018
Expanding the Diversity of Texts and Applications: Findings from the Section on Clinical Natural Language Processing of the International Medical Informatics Association Yearbook.
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 5 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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2022Pooled it
- 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
- A Question-and-Answer System to Extract Data From Free-Text Oncological Pathology Reports (CancerBERT Network): Development Study.Journal of medical Internet research · 2022Article
- Deep learning in clinical natural language processing: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2020Review
- Deep Learning-Based Structure-Activity Relationship Modeling for Multi-Category Toxicity Classification: A Case Study of 10K Tox21 Chemicals With High-Throughput Cell-Based Androgen Receptor Bioassay Data.Frontiers in physiology · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo summarize recent research and present a selection of the best papers published in 2017 in the field of clinical Natural Language Processing (NLP).
methodsA 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.
resultsClinical 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.
conclusionsClinical 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.
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.