Evidence map›Paper›PMID 26073888›Full record

SynthesisSystematic reviews2015

Automating data extraction in systematic reviews: a systematic review.

Siddhartha R Jonnalagadda, Pawan Goyal, Mark D Huffman

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Systematic reviews, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers, 14 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
83citing papers in PubMed, 14 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

83 citing papers in PubMed, 14 syntheses or guidelines pooled it.

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  5. The Cooperation Databank: Machine-Readable Science Accelerates Research Synthesis.Perspectives on psychological science : a journal of the Association for Psychological Science · 2022
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  9. Guideline
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  17. Review
  18. Evaluating AI Performance in Systematic Literature Reviews for HEOR: A Case Study.Journal of health economics and outcomes research · 2026
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  20. Review

23 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

3 authors.

Siddhartha R JonnalagaddaDivision of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 750 North Lake Shore Drive, 11th Floor, Chicago, IL, 60611, USA. sid@northwestern.edu.
Pawan GoyalDepartment of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, 721302, West Bengal, India. pawang@cse.iitkgp.ernet.in.
Mark D HuffmanDepartment of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, USA. m-huffman@northwestern.edu.

Funding

Improving the Efficiency and Efficacy in Authoring Essential Clinical FAQsR00LM011389 · NLM · NORTHWESTERN UNIVERSITY AT CHICAGO · PI JONNALAGADDA, SIDDHARTHA REDDY · 2013 to 2015
$665k
NLM NIH HHS 5R00LM011389NLM NIH HHS R00 LM011389
6 · The paper itself

Abstract

backgroundAutomation of the parts of systematic review process, specifically the data extraction step, may be an important strategy to reduce the time necessary to complete a systematic review. However, the state of the science of automatically extracting data elements from full texts has not been well described. This paper performs a systematic review of published and unpublished methods to automate data extraction for systematic reviews.

methodsWe systematically searched PubMed, IEEEXplore, and ACM Digital Library to identify potentially relevant articles. We included reports that met the following criteria: 1) methods or results section described what entities were or need to be extracted, and 2) at least one entity was automatically extracted with evaluation results that were presented for that entity. We also reviewed the citations from included reports.

resultsOut of a total of 1190 unique citations that met our search criteria, we found 26 published reports describing automatic extraction of at least one of more than 52 potential data elements used in systematic reviews. For 25 (48 %) of the data elements used in systematic reviews, there were attempts from various researchers to extract information automatically from the publication text. Out of these, 14 (27 %) data elements were completely extracted, but the highest number of data elements extracted automatically by a single study was 7. Most of the data elements were extracted with F-scores (a mean of sensitivity and positive predictive value) of over 70 %.

conclusionsWe found no unified information extraction framework tailored to the systematic review process, and published reports focused on a limited (1-7) number of data elements. Biomedical natural language processing techniques have not been fully utilized to fully or even partially automate the data extraction step of systematic reviews.

Indexed as

PublishingReview Literature as TopicData MiningHumansInformation Storage and RetrievalResearch Report

Identifiers

PMID26073888
PMCPMC4514954

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.