Year: 2025
Type: conference-paper
Abstract: Forecasting migraine attacks before they occur can significantly enhance the quality of life for patients by allowing them to take preemptive measures to mitigate or prevent the onset of symptoms. In this study, we aim to predict migraine attacks by leveraging the largest real-world migraine dataset available to date, collected from a mobile app between 2016 and 2022, it encompasses approximately 43,000 users and 7 million daily records gathered throughout the entire 6-year period. We introduce TRACE (Temporal Recurrent Autoencoder for Concept Embedding), a novel approach developed to anticipate migraine attacks. On two held-out sets, using 2-day and 3-day lookback data, TRACE successfully predicts approximately 70% of migraine episodes before their onset, outperforming competing methods in terms of sensitivity, i.e., accurately predicting migraine days. TRACE maintains a comparable specificity of around 60%, i.e., accurately predicting no-headache days. To understand the predictive factors driving models performance, we conduct both global and local feature importance analyses. Global importance is assessed using ablation analysis and Random Forest, while local importance is evaluated on a per-patient basis using SHapley Additive exPlanations (SHAP). Our SHAP analysis show that while common migraine triggers exist, individual triggers can vary significantly, highlighting the need for personalized predictive tools.
Authors: Israa Alqassem, Piyush Borole, Ammar Shaker, Ajitha Rajan
Institutions: NEC (Germany), University of Edinburgh
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4412568036