Evidence map›Paper›PMID 30477892›Full record

ArticleArtificial intelligence in medicine2019

Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification.

Imon Banerjee, Yuan Ling, Matthew C Chen, Sadid A Hasan, Curtis P Langlotz, Nathaniel Moradzadeh, Brian Chapman, Timothy Amrhein, David Mong, Daniel L Rubin and 2 more

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

Article in Artificial intelligence in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed, 4 pooled it
–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

65 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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5 more citing papers are in PubMed but not listed here.

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

12 authors.

Imon BanerjeeDepartment of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: imonb@stanford.edu.
Yuan LingArtificial Intelligence Laboratory, Philips Research North America, Cambridge, MA, USA.
Matthew C ChenDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Sadid A HasanArtificial Intelligence Laboratory, Philips Research North America, Cambridge, MA, USA.
Curtis P LanglotzDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Nathaniel MoradzadehDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Brian ChapmanDepartment of Bioinformatics, University of Utah Medical Center, UT, USA.
Timothy AmrheinDepartment of Neuroradiology, Duke University School of Medicine, NC, USA.
David MongDepartment of Radiology, Children Hospital Colorado, CO, USA.
Daniel L RubinDepartment of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Oladimeji FarriArtificial Intelligence Laboratory, Philips Research North America, Cambridge, MA, USA.
Matthew P LungrenDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.

Funding

Spectrum Stanford Center for clinical and Translational Research and EducationUL1TR001085 · NCATS · STANFORD UNIVERSITY · PI CULLEN, MARK RICHARD, GREENBERG, HARRY BERNARD · 2013 to 2017
$37.1M
Deep Learning for Pulmonary Embolism Imaging Decision Support: A Multi-institutional CollaborationR01LM012966 · NLM · STANFORD UNIVERSITY · PI SHAH, NIGAM H · 2018 to 2021
$1.4M
NCATS NIH HHS UL1 TR001085NLM NIH HHS R01 LM012966
6 · The paper itself

Abstract

This paper explores cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scale and compares them to the state-of-the-art domain-specific rule-based system - PEFinder and traditional machine learning methods - SVM and Adaboost. We proposed two distinct deep learning models - (i) CNN Word - Glove, and (ii) Domain phrase attention-based hierarchical recurrent neural network (DPA-HNN), for synthesizing information on pulmonary emboli (PE) from over 7370 clinical thoracic computed tomography (CT) free-text radiology reports collected from four major healthcare centers. Our proposed DPA-HNN model encodes domain-dependent phrases into an attention mechanism and represents a radiology report through a hierarchical RNN structure composed of word-level, sentence-level and document-level representations. Experimental results suggest that the performance of the deep learning models that are trained on a single institutional dataset, are better than rule-based PEFinder on our multi-institutional test sets. The best F1 score for the presence of PE in an adult patient population was 0.99 (DPA-HNN) and for a pediatrics population was 0.99 (HNN) which shows that the deep learning models being trained on adult data, demonstrated generalizability to pediatrics population with comparable accuracy. Our work suggests feasibility of broader usage of neural network models in automated classification of multi-institutional imaging text reports for a variety of applications including evaluation of imaging utilization, imaging yield, clinical decision support tools, and as part of automated classification of large corpus for medical imaging deep learning work.

Indexed as

Deep LearningNeural Networks, ComputerRadiography, ThoracicHumansInformation Storage and RetrievalPulmonary EmbolismConvolutional neural network (CNN)Pulmonary embolismRadiology report analysisRecurrent neural network (RNN)Text report classification

Identifiers

PMID30477892
PMCPMC6533167

What OpenQuestion holds

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Read underepoch 390

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.