ArticleBMC bioinformatics2017
Disorder recognition in clinical texts using multi-label structured SVM.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Article
- Natural language processing applied to mental illness detection: a narrative review.NPJ digital medicine · 2022Review
- Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF.BMC medical informatics and decision making · 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
backgroundInformation extraction in clinical texts enables medical workers to find out problems of patients faster as well as makes intelligent diagnosis possible in the future. There has been a lot of work about disorder mention recognition in clinical narratives. But recognition of some more complicated disorder mentions like overlapping ones is still an open issue. This paper proposes a multi-label structured Support Vector Machine (SVM) based method for disorder mention recognition. We present a multi-label scheme which could be used in complicated entity recognition tasks.
resultsWe performed three sets of experiments to evaluate our model. Our best F
conclusionsThis multi-label structured SVM based approach is demonstrated to work well with this disorder recognition task. The novel multi-label scheme we presented is superior to the baseline and it can be used in other models to solve various types of complicated entity recognition tasks as well.
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.