MyGraine: Predicting Migraines Through Various Machine Learning Models Utilizing User-Inputted Data

Zhu, Rebecca; Dave, Rucha

doi:10.36838/v2i4.13

Cited by 1 publication

(3 citation statements)

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“…The current research shows that artificial intelligence, simulation techniques, augmented and virtual reality, the Internet of Things, and cloud computing are becoming more popular in migraine research to facilitate the discovery of novel management options. For example, Zhu and Dave [ 87 ] used various machine learning models to predict migraine occurrences. They applied a data augmentation technique to a publicly available database and identified patterns and the most significant factors for predicting the occurrence of a migraine attack [ 87 ].…”

Section: Digital Twin Concept For Personalized Care For Migrainesmentioning

confidence: 99%

“…For example, Zhu and Dave [ 87 ] used various machine learning models to predict migraine occurrences. They applied a data augmentation technique to a publicly available database and identified patterns and the most significant factors for predicting the occurrence of a migraine attack [ 87 ]. These researchers [ 87 ] anticipated that federated learning can enable the use of their technique toward building individualized machine learning models based on individual migraine triggers.…”

Section: Digital Twin Concept For Personalized Care For Migrainesmentioning

confidence: 99%

“…They applied a data augmentation technique to a publicly available database and identified patterns and the most significant factors for predicting the occurrence of a migraine attack [ 87 ]. These researchers [ 87 ] anticipated that federated learning can enable the use of their technique toward building individualized machine learning models based on individual migraine triggers. Achieving this goal, however, needs a larger dataset to make such models more accurate.…”

Section: Digital Twin Concept For Personalized Care For Migrainesmentioning

confidence: 99%

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Intelligent Digital Twins for Personalized Migraine Care

Gazerani

2023

JPM

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Intelligent digital twins closely resemble their real-life counterparts. In health and medical care, they enable the real-time monitoring of patients, whereby large amounts of data can be collected to produce actionable information. These powerful tools are constructed with the aid of artificial intelligence, machine learning, and deep learning; the Internet of Things; and cloud computing to collect a diverse range of digital data (e.g., from digital patient journals, wearable sensors, and digitized monitoring equipment or processes), which can provide information on the health conditions and therapeutic responses of their physical twins. Intelligent digital twins can enable data-driven clinical decision making and advance the realization of personalized care. Migraines are a highly prevalent and complex neurological disorder affecting people of all ages, genders, and geographical locations. It is ranked among the top disabling diseases, with substantial negative personal and societal impacts, but the current treatment strategies are suboptimal. Personalized care for migraines has been suggested to optimize their treatment. The implementation of intelligent digital twins for migraine care can theoretically be beneficial in supporting patient-centric care management. It is also expected that the implementation of intelligent digital twins will reduce costs in the long run and enhance treatment effectiveness. This study briefly reviews the concept of digital twins and the available literature on digital twins for health disorders such as neurological diseases. Based on these, the potential construction and utility of digital twins for migraines will then be presented. The potential and challenges when implementing intelligent digital twins for the future management of migraines are also discussed.

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Section: Digital Twin Concept For Personalized Care For Migrainesmentioning

confidence: 99%