Evidence map›Paper›PMID 31437913›Full record

ArticleStudies in health technology and informatics2019

Recurrent Deep Network Models for Clinical NLP Tasks: Use Case with Sentence Boundary Disambiguation.

Benjamin C Knoll, Elizabeth A Lindemann, Arian L Albert, Genevieve B Melton, Serguei V S Pakhomov

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Studies in health technology and informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Benjamin C KnollInstitute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.
Elizabeth A LindemannDepartment of Surgery, University of Minnesota, Minneapolis, Minnesota, USA.
Arian L AlbertInstitute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.
Genevieve B MeltonInstitute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.
Serguei V S PakhomovInstitute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.

Funding

University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR002494 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R, WEISDORF, DANIEL J · 2018 to 2022
$34.9M
Open Health Natural Language Processing CollaboratoryU01TR002062 · NCATS · MAYO CLINIC ROCHESTER · PI JIANG, XIAOQIAN, LIU, HONGFANG · 2017 to 2021
$7.6M
Natural language processing for clinical and translational researchR01GM102282 · NIGMS · MAYO CLINIC ROCHESTER · PI LIU, HONGFANG, PAKHOMOV, SERGUEI VS · 2013 to 2016
$2.5M
AHRQ HHS R01 HS022085NCATS NIH HHS U01 TR002062NCATS NIH HHS UL1 TR002494NIGMS NIH HHS R01 GM102282
6 · The paper itself

Abstract

Although a number of foundational natural language processing (NLP) tasks like text segmentation are considered a simple problem in the general English domain dominated by well-formed text, complexities of clinical documentation lead to poor performance of existing solutions designed for the general English domain. We present an alternative solution that relies on a convolutional neural network layer followed by a bidirectional long short-term memory layer (CNN-Bi-LSTM) for the task of sentence boundary disambiguation and describe an ensemble approach for domain adaptation using two training corpora. Implementations using the Keras neural-networks API are available at https://github.com/NLPIE/clinical-sentences.

Indexed as

Natural Language ProcessingNeural Networks, ComputerDocumentationLanguageMachine LearningNatural Language ProcessingNeural Networks (Computer)

Identifiers

PMID31437913
PMCPMC7360019

What OpenQuestion holds

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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.