Machine learning approach to predict medication overuse in migraine patients

https://doi.org/10.1016/j.csbj.2020.06.006Get rights and content
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Highlights

  • Medication overuse is related to chronicization and medication-overuse headache.

  • Prediction of medication overuse (MO) is a challenge in the management of migraine.

  • Machine learning and random optimization could help to estimate MO risk in migraine.

  • A customized decision support system was devised for migraine clinical management.

  • This approach may exploit significant patterns in data connoting causality.

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.

Keywords

Migraine
Medication overuse
Artificial intelligence
Machine learning
Decision support systems

Abbreviations

AI
Artificial Intelligence
AUC
Area Under the Curve
BMI
body mass index
CI
Confidence Interval
DBH 19-bp I/D polymorphism
Dopamine-Beta-Hydroxylase 19 bp insertion/deletion polymorphism
DSS
Decision Support System
ICT
Information and Communications Technology
KELP
Kernel-based Learning Platform
LRs
likelihood ratios
MKL
Multiple Kernel Learning
ML
Machine Learning
MO
Medication Overuse
NSAID
nonsteroidal anti-inflammatory drugs
PVI
Predictive Value Imputation
ROC
Receiver operating characteristic
SE
Standard Error
SVM
Support Vector Machine
RO
Random Optimization

Cited by (0)

1

Co-first authors for equal contribution.

2

Co-last authors for equal contribution.