Evidence map›Paper›PMID 34457146›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science

A Discrete Joint Model for Entity and Relation Extraction from Clinical Notes.

Zongcheng Ji, Omid Ghiasvand, Stephen Wu, Hua Xu

Abstract read

In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

BIR: Biomedical Information Retrieval System for Cancer Treatment in Electronic Health Record Using Transformers.Sensors (Basel, Switzerland) · 2023
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zongcheng JiSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.

Omid GhiasvandSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.

Stephen WuSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.

Hua XuSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extracting clinical concepts and their relations from clinical narratives is one of the fundamental tasks in clinical natural language processing. Traditional solutions often separate this task into two subtasks with a pipeline architecture, which first recognize the named entities and then classify the relations between any possible entity pairs. The pipeline architecture, although widely used, has two limitations: 1) it suffers from error propagation from the recognition step to the classification step, 2) it cannot utilize the interactions between the two steps. To address the limitations, we investigated a discrete joint model based on structured perceptron and beam search to jointly perform named entity recognition (NER) and relation classification (RC) from clinical notes.

Indexed as

Natural Language ProcessingNeural Networks, ComputerHumansNarrationResearch Design

Identifiers

PMID34457146

PMCPMC8378610

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

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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.