Evidence map›Paper›PMID 32514380›Full record

ReviewCardiac failure review2020

Artificial Intelligence, Data Sensors and Interconnectivity: Future Opportunities for Heart Failure.

Patrik Bachtiger, Carla M Plymen, Punam A Pabari, James P Howard, Zachary I Whinnett, Felicia Opoku, Stephen Janering, Aldo A Faisal, Darrel P Francis, Nicholas S Peters

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cardiac failure review, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
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  9. Article
  10. Current and Future Use of Artificial Intelligence in Electrocardiography.Journal of cardiovascular development and disease · 2023
    Review
  11. Review
  12. Article
  13. 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

10 authors.

Patrik BachtigerImperial Centre for Cardiac Engineering, National Heart and Lung Institute, Imperial College London, UK.
Carla M PlymenDepartment of Cardiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital London, UK.
Punam A PabariDepartment of Cardiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital London, UK.
James P HowardImperial Centre for Cardiac Engineering, National Heart and Lung Institute, Imperial College London, UK.
Zachary I WhinnettDepartment of Cardiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital London, UK.
Felicia OpokuIT Department, Imperial College Healthcare NHS London, UK.
Stephen JaneringIT Department, Imperial College Healthcare NHS London, UK.
Aldo A FaisalDepartments of Bioengineering and Computing, Data Science Institute, Imperial College London, UK.
Darrel P FrancisImperial Centre for Cardiac Engineering, National Heart and Lung Institute, Imperial College London, UK.
Nicholas S PetersImperial Centre for Cardiac Engineering, National Heart and Lung Institute, Imperial College London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A higher proportion of patients with heart failure have benefitted from a wide and expanding variety of sensor-enabled implantable devices than any other patient group. These patients can now also take advantage of the ever-increasing availability and affordability of consumer electronics. Wearable, on- and near-body sensor technologies, much like implantable devices, generate massive amounts of data. The connectivity of all these devices has created opportunities for pooling data from multiple sensors - so-called interconnectivity - and for artificial intelligence to provide new diagnostic, triage, risk-stratification and disease management insights for the delivery of better, more personalised and cost-effective healthcare. Artificial intelligence is also bringing important and previously inaccessible insights from our conventional cardiac investigations. The aim of this article is to review the convergence of artificial intelligence, sensor technologies and interconnectivity and the way in which this combination is set to change the care of patients with heart failure.

Indexed as

Artificial intelligenceconnected caredata sensorsdeep learningheart failuremachine learningremote monitoring

Identifiers

PMID32514380
PMCPMC7265101

What OpenQuestion holds

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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.