期刊名称：Computational and Structural Biotechnology Journal
印刷版ISSN：2001-0370
出版年度：2020
卷号：18
页码：1487-1496
DOI：10.1016/j.csbj.2020.06.006
出版社：Computational and Structural Biotechnology Journal
摘要：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.
关键词：Migraine ; Medication overuse ; Artificial intelligence ; Machine learning ; Decision support systems ; 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
