Evidence map›Paper›PMID 36329678›Full record

SynthesisJournal of nursing management2022

Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature.

Lucija Gosak, Kristina Martinović, Mateja Lorber, Gregor Stiglic

Open access · hybridAbstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Journal of nursing management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 3 pooled it
5.1field-weighted citation impact, top 4% of its field
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

5 citing papers in PubMed, 3 syntheses or guidelines pooled it, 21 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Review
  5. Review
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 at 2 institutions in 2 countries.

Lucija GosakFaculty of Health Sciences, University of Maribor, Maribor, Slovenia.ORCID https://orcid.org/0000-0002-8742-6594
Kristina MartinovićFaculty of Health Sciences, University of Maribor, Maribor, Slovenia.
Mateja LorberFaculty of Health Sciences, University of Maribor, Maribor, Slovenia.ORCID https://orcid.org/0000-0001-7200-5204
Gregor StiglicFaculty of Health Sciences, University of Maribor, Maribor, Slovenia.ORCID https://orcid.org/0000-0002-0183-8679
University of Maribor · SIUniversity of Primorska · SI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimThe aim of this review is to examine the effectiveness of artificial intelligence in predicting multimorbid diabetes-related complications.

backgroundIn diabetic patients, several complications are often present, which have a significant impact on the quality of life; therefore, it is crucial to predict the level of risk for diabetes and its complications. EVALUATION: International databases PubMed, CINAHL, MEDLINE and Scopus were searched using the terms artificial intelligence, diabetes mellitus and prediction of complications to identify studies on the effectiveness of artificial intelligence for predicting multimorbid diabetes-related complications. The results were organized by outcomes to allow more efficient comparison. KEY ISSUES: Based on the inclusion/exclusion criteria, 11 articles were included in the final analysis. The most frequently predicted complications were diabetic neuropathy (n = 7). Authors included from two to a maximum of 14 complications. The most commonly used prediction models were penalized regression, random forest and Naïve Bayes model neural network.

conclusionThe use of artificial intelligence can predict the risks of diabetes complications with greater precision based on available multidimensional datasets and provides an important tool for nurses working in preventive health care. IMPLICATIONS FOR NURSING MANAGEMENT: Using artificial intelligence contributes to a better quality of care, better autonomy of patients in diabetes management and reduction of complications, costs of medical care and mortality.

Indexed as

Artificial IntelligenceDiabetes MellitusBayes TheoremHumansQuality of Lifeartificial intelligencediabetesprediction modelsprediction of diabetes complications

Identifiers

PMID36329678
PMCPMC10100477
OpenAlexW4308179522

What Socratic 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 Socratic graph.