Identifying Predictors of CGRP Treatment Response in Migraine Using Machine Learning on Electronic Health Records (P10-15.019)

Phillip Phan; Scott Keith; Michael Li; Richard Hass; Hsiangkuo Yuan

doi:10.1212/wnl.0000000000217646

June 9, 2026Neurology

Identifying Predictors of CGRP Treatment Response in Migraine Using Machine Learning on Electronic Health Records (P10-15.019)

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Key Points

The research aims to identify predictors of response to CGRP-targeting treatments for migraines using electronic health records.
Developed machine learning models utilizing electronic health record data.
Evaluated the accuracy of models in predicting treatment responses to CGRP medications.
Identified specific predictors significantly associated with better responses to CGRP treatment.
Demonstrated that machine learning can effectively analyze large patient data for treatment outcomes.

Implication

Evaluates machine learning models to predict migraine treatment response in migraineurs, suggesting improved personalized care.

Abstract

To develop and evaluate machine learning models for predicting early clinical response to calcitonin gene-related peptide (CGRP)-targeting migraine medications using routinely collected electronic health record data.

Discussion

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Cite This Study

Phan et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf3d6faef96ed7f0564df https://doi.org/10.1212/wnl.0000000000217646

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Identifying Predictors of CGRP Treatment Response in Migraine Using Machine Learning on Electronic Health Records (P10-15.019)

Key Points

Implication

Abstract

Discussion

Cite This Study

Identifying Predictors of CGRP Treatment Response in Migraine Using Machine Learning on Electronic Health Records (P10-15.019)

Key Points

Implication

Abstract

Discussion

Cite This Study

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