Evidence map›Paper›PMID 27257386›Full record

ArticleBiomedical informatics insights2016

Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning.

Kevin Bretonnel Cohen, Benjamin Glass, Hansel M Greiner, Katherine Holland-Bouley, Shannon Standridge, Ravindra Arya, Robert Faist, Diego Morita, Francesco Mangano, Brian Connolly and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical informatics insights, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

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

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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.

Kevin Bretonnel CohenComputational Bioscience Program, University of Colorado School of Medicine, Denver, CO, USA.
Benjamin GlassDivision of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.
Hansel M GreinerDivision of Neurology, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.
Katherine Holland-BouleyDivision of Neurology, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.
Shannon StandridgeDivision of Neurology, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.
Ravindra AryaDivision of Neurology, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.
Robert FaistDivision of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.
Diego MoritaDivision of Neurology, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.
Francesco ManganoDivision of Pediatric Neurosurgery, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.
Brian ConnollyDivision of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.
Tracy GlauserDivision of Neurology, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.
John PestianDivision of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveWe describe the development and evaluation of a system that uses machine learning and natural language processing techniques to identify potential candidates for surgical intervention for drug-resistant pediatric epilepsy. The data are comprised of free-text clinical notes extracted from the electronic health record (EHR). Both known clinical outcomes from the EHR and manual chart annotations provide gold standards for the patient's status. The following hypotheses are then tested: 1) machine learning methods can identify epilepsy surgery candidates as well as physicians do and 2) machine learning methods can identify candidates earlier than physicians do. These hypotheses are tested by systematically evaluating the effects of the data source, amount of training data, class balance, classification algorithm, and feature set on classifier performance. The results support both hypotheses, with F-measures ranging from 0.71 to 0.82. The feature set, classification algorithm, amount of training data, class balance, and gold standard all significantly affected classification performance. It was further observed that classification performance was better than the highest agreement between two annotators, even at one year before documented surgery referral. The results demonstrate that such machine learning methods can contribute to predicting pediatric epilepsy surgery candidates and reducing lag time to surgery referral.

Indexed as

epilepsyepilepsy surgerymachine learningnatural language processingneurosurgery

Identifiers

PMID27257386
PMCPMC4876984

What OpenQuestion holds

Textmetadata
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.