Evidence map›Paper›PMID 28494618›Full record

ArticleJournal of diabetes science and technology2018

Machine Learning Methods to Predict Diabetes Complications.

Arianna Dagliati, Simone Marini, Lucia Sacchi, Giulia Cogni, Marsida Teliti, Valentina Tibollo, Pasquale De Cata, Luca Chiovato, Riccardo Bellazzi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of diabetes science and technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 135 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
135citing papers in PubMed, 11 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

135 citing papers in PubMed, 11 syntheses or guidelines pooled it.

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75 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

9 authors.

Arianna Dagliati1 Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
Simone Marini1 Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
Lucia Sacchi1 Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
Giulia Cogni3 IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy.
Marsida Teliti3 IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy.
Valentina Tibollo3 IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy.
Pasquale De Cata3 IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy.
Luca Chiovato3 IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy.
Riccardo Bellazzi1 Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice.

Indexed as

AlgorithmsDiabetes ComplicationsMachine LearningData MiningDiabetes Mellitus, Type 2HumansData MiningMachine LearningMicrovascular ComplicationsRisk PredictionsType 2 Diabetes

Identifiers

PMID28494618
PMCPMC5851210

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.