Evidence map›Paper›PMID 26851618›Full record

ArticleAccident; analysis and prevention2016

Computerized "Learn-As-You-Go" classification of traumatic brain injuries using NEISS narrative data.

Wei Chen, Krista K Wheeler, Simon Lin, Yungui Huang, Huiyun Xiang

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Accident; analysis and prevention, 2016. 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.

  1. 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.

Wei ChenResearch Information Solutions and Innovation, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA.
Krista K WheelerCenter for Injury Research and Policy, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA; Center for Pediatric Trauma Research, Nationwide Children's Hospital, Columbus, OH, USA.
Simon LinResearch Information Solutions and Innovation, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA.
Yungui HuangResearch Information Solutions and Innovation, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA.
Huiyun XiangCenter for Injury Research and Policy, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA; Center for Pediatric Trauma Research, Nationwide Children's Hospital, Columbus, OH, USA. Electronic address: huiyun.xiang@nationwidechildrens.org.

Funding

The Ohio State University Center for clinical and Translational ScienceUL1TR001070 · NCATS · OHIO STATE UNIVERSITY · PI JACKSON, REBECCA D · 2013 to 2017
$22.6M
(CDC R49) Center for Injury Research and PolicyR49CE002106 · CE · RESEARCH INST NATIONWIDE CHILDREN'S HOSP · PI SMITH, GARY A. · 2012 to 2018
$5.5M
National Study of Undertriage of Trauma PatientsR03HS022277 · AHRQ · RESEARCH INST NATIONWIDE CHILDREN'S HOSP · PI XIANG, HENRY · 2013 to 2014
$100k
ACL HHS R49CE002106AHRQ HHS 1R03HS022277AHRQ HHS R03 HS022277NCATS NIH HHS UL1 TR001070NCATS NIH HHS UL1TR001070NCIPC CDC HHS 1R49CE002106NCIPC CDC HHS R49 CE002106
6 · The paper itself

Abstract

One important routine task in injury research is to effectively classify injury circumstances into user-defined categories when using narrative text. However, traditional manual processes can be time consuming, and existing batch learning systems can be difficult to utilize by novice users. This study evaluates a "Learn-As-You-Go" machine-learning program. When using this program, the user trains classification models and interactively checks on accuracy until a desired threshold is reached. We examined the narrative text of traumatic brain injuries (TBIs) in the National Electronic Injury Surveillance System (NEISS) and classified TBIs into sport and non-sport categories. Our results suggest that the DUALIST "Learn-As-You-Go" program, which features a user-friendly online interface, is effective in injury narrative classification. In our study, the time frame to classify tens of thousands of narratives was reduced from a few days to minutes after approximately sixty minutes of training.

Indexed as

Machine LearningAthletic InjuriesBrain InjuriesDatabases, FactualHumansModels, TheoreticalUnited StatesComputerized classificationMachine learningSports injury classificationTraumatic brain injuriesUnstructured NEISS narratives

Identifiers

PMID26851618
PMCPMC5119271

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Registered trials

None linked

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.