The promise of machine learning in predicting migraine attacks

Background This narrative review establishes the current state of the art of machine learning approaches for prediction of migraine attacks. Related concepts are highlighted including the identification of triggers or premonitory symptoms and methods for evaluating prediction models. Existing efforts at machine learning prediction of individual migraine headaches and attacks are reviewed in detail. Challenges in this task are discussed. Results A variety of input data and modeling approaches have been used. It is consistently found that individualized models provide better results compared to a generalized model and achievable performance varies considerably between individuals. Patient needs should be assessed to discover what a valuable prediction looks like. The field should develop common standards for evaluating migraine prediction algorithms. Conclusions/Interpretations While the problem is far from solved there is great potential and reason to believe that feasible solutions that improve the quality of life of those with migraine are within our grasp.