Evidence map›Paper›PMID 26293849›Full record

ReviewYearbook of medical informatics2015

Health Informatics via Machine Learning for the Clinical Management of Patients.

D A Clifton, K E Niehaus, P Charlton, G W Colopy

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Yearbook of medical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Guideline
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Machine learning in patient flow: a review.Progress in biomedical engineering (Bristol, England) · 2021
    Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Article
  15. Article
  16. Artificial intelligence and nuclear medicine.Nuclear medicine communications · 2019
    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

4 authors.

D A CliftonDavid A. Clifton, Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK, E-mail: davidc@robots.ox.ac.uk.
K E Niehaus
P Charlton
G W Colopy

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo review how health informatics systems based on machine learning methods have impacted the clinical management of patients, by affecting clinical practice.

methodsWe reviewed literature from 2010-2015 from databases such as Pubmed, IEEE xplore, and INSPEC, in which methods based on machine learning are likely to be reported. We bring together a broad body of literature, aiming to identify those leading examples of health informatics that have advanced the methodology of machine learning. While individual methods may have further examples that might be added, we have chosen some of the most representative, informative exemplars in each case.

resultsOur survey highlights that, while much research is taking place in this high-profile field, examples of those that affect the clinical management of patients are seldom found. We show that substantial progress is being made in terms of methodology, often by data scientists working in close collaboration with clinical groups.

conclusionsHealth informatics systems based on machine learning are in their infancy and the translation of such systems into clinical management has yet to be performed at scale.

Indexed as

Data MiningElectronic Health RecordsMachine LearningPatient Care ManagementHumansIntensive Care UnitsMedical Informaticsdata miningelectronic health recordsHealth informaticsinformation systems

Identifiers

PMID26293849
PMCPMC4587065

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.