ArticleDigital health
The association between social risk profile and self-reported severe headache or migraine with all-cause mortality risk: A machine learning-based prediction model and interpretability analysis.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Social risk factors are key determinants of migraine occurrence and progression. This study assessed the association between the social risk profile (SRP) and the prevalence of self-reported severe headache or migraine and all-cause mortality in US adults, and developed machine learning prediction models to explore feature contributions to internal risk stratification. Methods: Using data from the National Health and Nutrition Examination Survey (NHANES) 1999-2004, weighted multivariate logistic regression evaluated the SRP-migraine association, and a weighted Cox proportional hazards model assessed the influence of SRP on all-cause mortality among migraine patients. The Boruta and Lasso algorithms selected predictive features for nine machine learning classifiers to predict migraine risk and four survival models to assess mortality risk. SMOTE was applied within cross-validation folds to address class imbalance. SHAP values were utilized to identify the most critical features. Results: Among 11,861 participants, 2,355 self-reported severe headache or migraine; 2,351 were included in the mortality analysis after excluding 4 individuals with missing survival status. Over a median follow-up of 206 months (IQR: 187-224), 471 deaths occurred. Higher SRP scores were associated with lower migraine prevalence (OR = 0.44, 95% CI: 0.34-0.57) and lower all-cause mortality (HR = 0.31, 95% CI: 0.19-0.50). XGBoost achieved the best performance for migraine prediction (AUC = 0.732, 95% CI: 0.712-0.753), while Random Survival Forest performed best for mortality prediction (AUC = 0.882). SHAP analysis identified age, SRP, and cotinine as key predictors. Decision curve and calibration analyses demonstrated acceptable internal performance, supported by ten-fold cross-validation. Conclusion: SRP is an independent predictor of migraine risk and long-term survival. The machine learning analysis provides exploratory insights into feature importance for risk stratification within the development sample, while the regression-based association estimates support the epidemiological significance of social determinants in migraine.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.