ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2019
Learning Inter-Sentence, Disorder-Centric, Biomedical Relationships from Medical Literature.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Approaches for text mining of mHealth literature.mHealth · 2022Article
- Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
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
Relationships between disorders and their associated tests, treatments and symptoms underpin essential information needs of clinicians and can support biomedical knowledge bases, information retrieval and ultimately clinical decision support. These relationships exist in the biomedical literature, however they are not directly available and have to be extracted from the text. Existing, automated biomedical relationship extraction methods tend to be narrow in scope, e.g., protein-protein interactions, and pertain to intra-sentence relationships. The proposed approach targets intra and inter-sentence, disorder-centric relationship extraction. It employs an LSTM deep learning model that utilises a novel, sequential feature set, including medical concept embeddings. The LSTM model outperforms rule based and co-occurrence models by at least +78% in F1 score, suggesting that inter-sentence relationships are an important subset of all disorder-centric relations and that our approach shows promise for inter-sentence relationship extraction in this and possibly other domains.
Indexed as
Identifiers
32308919PMC7153107What 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.