Machine learning approach to predict medication overuse in migraine patients

Autor: Fabio Massimo Zanzotto, Antonella Spila, Piero Barbanti, Gabriella Egeo, Patrizia Ferroni, Alessandro Rullo, Raffaele Palmirotta, Noemi Scarpato, Luisa Fofi, Fiorella Guadagni
Rok vydání: 2020
Předmět:
Artificial intelligence
Decision support system
BMI
body mass index

SVM
Support Vector Machine

Decision support systems
Logistic regression
Biochemistry
0302 clinical medicine
KELP
Kernel-based Learning Platform

Structural Biology
Medicine
0303 health sciences
NSAID
nonsteroidal anti-inflammatory drugs

RO
Random Optimization

Computer Science Applications
MKL
Multiple Kernel Learning

DBH 19-bp I/D polymorphism
Dopamine-Beta-Hydroxylase 19 bp insertion/deletion polymorphism

ML
Machine Learning

030220 oncology & carcinogenesis
Medication overuse
Research Article
Biotechnology
medicine.medical_specialty
lcsh:Biotechnology
MO
Medication Overuse

ROC
Receiver operating characteristic

Biophysics
DSS
Decision Support System

Discriminatory power
AI
Artificial Intelligence

03 medical and health sciences
lcsh:TP248.13-248.65
Internal medicine
LRs
likelihood ratios

Machine learning
ICT
Information and Communications Technology

Genetics
AUC
Area Under the Curve

Migraine
ComputingMethodologies_COMPUTERGRAPHICS
030304 developmental biology
PVI
Predictive Value Imputation

business.industry
medicine.disease
CI
Confidence Interval

SE
Standard Error

Risk evaluation
Weighting
Support vector machine
business
Zdroj: Computational and Structural Biotechnology Journal, Vol 18, Iss, Pp 1487-1496 (2020)
Computational and Structural Biotechnology Journal
ISSN: 2001-0370
DOI: 10.1016/j.csbj.2020.06.006
Popis: Graphical abstract
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.
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.
Databáze: OpenAIRE