Evidence map›Paper›PMID 32637046›Full record

ArticleComputational and structural biotechnology journal2020

Machine learning approach to predict medication overuse in migraine patients.

Patrizia Ferroni, Fabio M Zanzotto, Noemi Scarpato, Antonella Spila, Luisa Fofi, Gabriella Egeo, Alessandro Rullo, Raffaele Palmirotta, Piero Barbanti, Fiorella Guadagni

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In one paragraph

Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 4 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

30 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025
    Pooled it
  3. Pooled it
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  5. Review
  6. Article
  7. Article
  8. Article
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  10. Article
  11. Article
  12. Review
  13. Application of Artificial Intelligence in the Headache Field.Current pain and headache reports · 2024
    Review
  14. Review
  15. Review
  16. Article
  17. Review
  18. Review
  19. Article
  20. Intelligent Digital Twins for Personalized Migraine Care.Journal of personalized medicine · 2023
    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

10 authors.

Patrizia FerroniBioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy.
Fabio M ZanzottoDepartment of Enterprise Engineering, University of Rome "Tor Vergata", Viale Oxford 81, 00133 Rome, Italy.
Noemi ScarpatoDept. of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, Via di Val Cannuta 247, 00166 Rome, Italy.
Antonella SpilaBioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy.
Luisa FofiHeadache and Pain Unit, Dept. of Neurological, Motor and Sensorial Sciences, IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy.
Gabriella EgeoHeadache and Pain Unit, Dept. of Neurological, Motor and Sensorial Sciences, IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy.
Alessandro RulloNeatec S.p.A., Via Campi Flegrei, 34, 80078 Pozzuoli, Naples, Italy.
Raffaele PalmirottaDepartment of Biomedical Sciences & Human Oncology, University of Bari 'Aldo Moro', Bari, Italy.
Piero BarbantiDept. of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, Via di Val Cannuta 247, 00166 Rome, Italy.
Fiorella GuadagniBioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Machine learning (ML) is largely used to develop automatic predictors in migraine classification but automatic predictors for medication overuse (MO) in migraine are still in their infancy. Thus, to understand the benefits of ML in MO prediction, we explored an automated predictor to estimate MO risk in migraine. To achieve this objective, a study was designed to analyze the performance of a customized ML-based decision support system that combines support vector machines and Random Optimization (RO-MO). We used RO-MO to extract prognostic information from demographic, clinical and biochemical data. Using a dataset of 777 consecutive migraine patients we derived a set of predictors with discriminatory power for MO higher than that observed for baseline SVM. The best four were incorporated into the final RO-MO decision support system and risk evaluation on a five-level stratification was performed. ROC analysis resulted in a c-statistic of 0.83 with a sensitivity and specificity of 0.69 and 0.87, respectively, and an accuracy of 0.87 when MO was predicted by at least three RO-MO models. Logistic regression analysis confirmed that the derived RO-MO system could effectively predict MO with ORs of 5.7 and 21.0 for patients classified as probably (3 predictors positive), or definitely at risk of MO (4 predictors positive), respectively. In conclusion, a combination of ML and RO - taking into consideration clinical/biochemical features, drug exposure and lifestyle - might represent a valuable approach to MO prediction in migraine and holds the potential for improving model precision through weighting the relative importance of attributes.

Indexed as

AI, Artificial IntelligenceArtificial intelligenceAUC, Area Under the CurveBMI, body mass indexCI, Confidence IntervalDBH 19-bp I/D polymorphism, Dopamine-Beta-Hydroxylase 19 bp insertion/deletion polymorphismDecision support systemsDSS, Decision Support SystemICT, Information and Communications TechnologyKELP, Kernel-based Learning PlatformLRs, likelihood ratiosMachine learningMedication overuseMigraineMKL, Multiple Kernel LearningML, Machine LearningMO, Medication OveruseNSAID, nonsteroidal anti-inflammatory drugsPVI, Predictive Value ImputationROC, Receiver operating characteristicRO, Random OptimizationSE, Standard ErrorSVM, Support Vector Machine

Identifiers

PMID32637046
PMCPMC7327028

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.