Evidence map›Paper›PMID 34499041›Full record

ReviewJMIR medical informatics2021

Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review.

Wael Abdelkader, Tamara Navarro, Rick Parrish, Chris Cotoi, Federico Germini, Alfonso Iorio, R Brian Haynes, Cynthia Lokker

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Preparing Allergists to Practice in 2050 Using Artificial Intelligence.The journal of allergy and clinical immunology. In practice · 2025
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Overcoming the challenges of using automated technologies for public health evidence synthesis.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2023
    Review
  11. K-RET: knowledgeable biomedical relation extraction system.Bioinformatics (Oxford, England) · 2023
    Article
  12. Review
  13. Review
  14. Article
  15. 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

8 authors.

Wael Abdelkader *Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.ORCID https://orcid.org/0000-0002-9581-1521
Tamara Navarro *Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.ORCID https://orcid.org/0000-0003-2810-6942
Rick Parrish *Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.ORCID https://orcid.org/0000-0002-3809-6015
Chris Cotoi *Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.ORCID https://orcid.org/0000-0001-7029-0582
Federico Germini *Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.ORCID https://orcid.org/0000-0002-0802-3616
Alfonso Iorio *Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.ORCID https://orcid.org/0000-0002-3331-8766
R Brian Haynes *Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.ORCID https://orcid.org/0000-0002-1453-3196
Cynthia Lokker *Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.ORCID https://orcid.org/0000-0003-2436-4290

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy.

objectiveThe goal of the research was to summarize the nature and comparative performance of machine learning approaches that have been applied to retrieve high-quality evidence for clinical consideration from the biomedical literature.

methodsWe conducted a systematic review of studies that applied machine learning techniques to identify high-quality clinical articles in the biomedical literature. Multiple databases were searched to July 2020. Extracted data focused on the applied machine learning model, steps in the development of the models, and model performance.

resultsFrom 3918 retrieved studies, 10 met our inclusion criteria. All followed a supervised machine learning approach and applied, from a limited range of options, a high-quality standard for the training of their model. The results show that machine learning can achieve a sensitivity of 95% while maintaining a high precision of 86%.

conclusionsMachine learning approaches perform well in retrieving high-quality clinical studies. Performance may improve by applying more sophisticated approaches such as active learning and unsupervised machine learning approaches.

Indexed as

accuracybioinformaticsclinical careclinical supportevidence-based medicineinformation retrievalliterature databasesmachine learningmedical literaturesystematic review

Identifiers

PMID34499041
PMCPMC8461527

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.