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 |
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| 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 |
| 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 |
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