- A new study analyzing a very large real-world dataset of Nerivio app users suggests that the AI model can predict next-day migraine risk with 91.2% accuracy.
- The model found that a person’s headache severity and patterns over the previous 30 days were more informative than prodromal (early) symptoms immediately before an attack.
- The researchers highlight that the technology is intended to help people prepare, not diagnose or change migraine treatment, and note that further research is still necessary.
Migraine is very common in the United States, affecting roughly
Migraine attacks often begin with a prodromal phase, which can occur hours or even days before migraine symptoms. Common prodromal signs can include fatigue, yawning, mood or cognitive changes, neck stiffness, food cravings, nausea, and sensitivity to light or sound.
An individual’s recurring prodromal pattern often serves as an early warning that an episode may be developing. Identifying these personal warning signs may allow a person to recognize an impending attack and, when appropriate, initiate an early treatment plan to help reduce the severity or duration of the attack for some people.
Now, a new study suggests that a machine learning model trained on data from more than 53,000 people with migraine could predict whether a person would experience a migraine the following day with 91% accuracy.
Notably, the findings, published in Neurology Open Access, suggest that a person’s headache patterns over the previous month may provide more useful information about their next-day migraine risk than prodromal symptoms.
Predicting a migraine before it starts
Rather than focusing primarily on prodromal symptoms, the researchers examined longer-term patterns in people’s symptom history. They analyzed data from 53,065 Nerivio app users, resulting in 770,473 daily reports collected between January 2020 and July 2025.
The dataset included information from electronic migraine diaries, questionnaires, demographic characteristics, and location-based weather data.
The researchers then tested 7 machine learning algorithms to determine whether these data could identify people who were likely to experience a migraine the following day. The best-performing model was a customized version of an algorithm called XGBoost.
The model achieved 91.2% accuracy, meaning that when it predicted that a person was likely to experience a migraine the following day, that prediction was correct about 91% of the time in the study dataset. The model also achieved:
- 81% overall accuracy
- 80% sensitivity, measuring how well it identified next-day migraine events
- 83% specificity, measuring how well it identified days without a migraine
- 0.893 area under the curve (AUC), a commonly used measure of how well a prediction model distinguishes between two outcomes
The researchers note that the outcome included headaches ranging from mild to moderate and severe pain, rather than being limited to more disabling migraine attacks.
A person’s own headache history is particularly important
One of the study’s notable findings was that the strongest predictive information did not come from prodromal symptoms. Instead, the model placed substantial weight on a person’s headache patterns over the preceding 30 days.
In particular, the rolling average of headache severity during the previous month was the most influential individual feature. Features calculated from the 30 days before an attack collectively accounted for about 56% of the model’s performance. By comparison, information on prodromal symptoms accounted for about 11%.
This suggests that migraine risk may be influenced not only by what happens shortly before an attack, but also by patterns that emerge over several weeks.
Could migraine prediction help patients?
Being able to anticipate a migraine could potentially give people more time to prepare for an episode.
For example, someone who knows their risk may be elevated the following day might be able to plan their schedule, prioritize sleep, maintain regular meals and hydration, or ensure they have their prescribed treatments available.
The study authors suggest that better forecasting could also potentially reduce some of the uncertainty and anxiety that people experience between migraine attacks.
However, the artificial intelligence (AI) tool is intended to provide information about the likelihood of a migraine rather than to make treatment decisions.
In a press release, Theranica, which owns and develops the Nerivio app, states its ‘Your Day Ahead’ feature, which incorporates the machine learning model into the app, does not diagnose migraine or recommend changes to prescribed treatment. It is also worth noting that many of the study authors are also affiliated with Theranica.
Medical News Today spoke with Vernon Williams, MD, sports neurologist and founding director of the Center for Sports Neurology and Pain Medicine at Cedars-Sinai Orthopedics and Sports Medicine in Los Angeles and B-ASE Performance, Inc, who was not involved in the study, about what a person may consider doing differently if the app predicts that someone’s migraine risk tomorrow is high.
“If the app predicts that someone’s migraine risk will be high the following day, that can be a useful reminder to be especially consistent with the lifestyle habits that help reduce migraine risk,” he noted.
“Getting adequate, regular sleep, staying well hydrated, eating nutritious meals at regular intervals, and minimizing unnecessary stress can help raise an individual’s threshold for developing a migraine. In other words, the goal is to make the brain less susceptible to known triggers.”
—Vernon Williams, M.D.
“It is also important to pay attention to personal migraine patterns. If someone knows that certain foods, alcohol, skipped meals, lack of sleep, or other factors commonly trigger their migraine (episodes), a high risk prediction may be a good reason to be particularly diligent about avoiding those triggers,” Williams said.
“That said, people should remember that migraine-prediction apps are tools, not diagnostic devices. Their predictions are based on the information and algorithms available to them and may not accurately predict what will happen for every individual,” he highlighted.
Williams also stressed that the app, diaries/journals, and lifestyle measures alone may not be enough for people who experience frequent and disabling migraine episodes.
“There are effective preventive medications that can reduce migraine frequency, as well as medications designed to stop an attack once it begins. Anyone experiencing frequent (migraine) should discuss these options with their healthcare provider to develop an individualized treatment plan,” he said.
What are the limitations?
Despite the large size of the dataset, the findings do not establish that the model will perform equally well for every person with migraine.
The participants were all users of the Nerivio app, meaning the study population may differ from the broader population of people living with migraine. Additionally, the cohort also tended toward people with more frequent migraine and those who were actively engaged with treatment.
Therefore, the researchers note that the findings need to be confirmed in broader migraine populations.
Another important consideration is that a prediction model is not the same as a diagnostic test or a treatment. A prediction of elevated migraine risk does not guarantee an attack will occur, and a low predicted risk does not necessarily mean an attack cannot occur.
“A high-risk prediction does not mean a migraine is inevitable, and a low-risk prediction does not guarantee that one will not occur. People should use these predictions as an additional source of information rather than allowing an app to replace their own experience, clinical judgment, or advice from their healthcare provider.”
—Vernon Williams, M.D.
The study was also based on observational, real-world app data rather than a randomized clinical trial testing whether acting on the predictions improves migraine outcomes.
What could come next for migraine prediction?
The findings provide evidence that longitudinal, patient-generated data may contain useful signals for predicting migraine attacks.
Rather than relying solely on prodromal symptoms, future migraine prediction tools could potentially combine information about headache frequency, severity, recent attack patterns, individual characteristics, and environmental factors.
For people living with migraine, the practical value may ultimately depend on whether more accurate forecasting leads to meaningful improvements in daily life, such as better preparation, fewer disruptions, reduced anxiety, or improved management of attacks.
For now, the results represent that machine learning can identify patterns in routinely collected migraine diary data and use those patterns to provide next-day risk information, rather than replacing clinical diagnosis or prescribing decisions.
