[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118469-en":3,"doc-seo-118469-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118469,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Forecasting migraine with machine learning based on mobile phone diary and wearable data","The study develops and evaluates machine-learning models that forecast next-day migraine attacks by combining preictal headache diary entries with simple physiological measurements collected through a smartphone app and wearable devices. In a prospective usability and development setting, 18 patients generated hundreds of diary entries alongside wireless biofeedback capturing heart rate, peripheral skin temperature, and muscle tension. Among multiple architectures, a random-forest approach produced the highest performance, with a modest area under the ROC curve on a hold-out dataset. ","Original Article  \nForecasting migraine with machine learning based on mobile phone diary and wearable data  \nAnker Stubberud 1,2 , Sigrid Hegna Ingvaldsen 1,3,  \nEiliv Brenner4, Ingunn Winnberg4, Alexander Olsen2,3,5, Gøril Bruvik Gravdahl 1,2,4 , Manjit Singh Matharu 1,2,6 , Parashkev Nachev6 and Erling Tronvik 1,2,4  \nCephalalgia  \n2023, Vol. 43(5) 1–10  \n! International Headache Society 2023 Article reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/03331024231169244](DOI: 10.1177/03331024231169244)[ ](DOI: 10.1177/03331024231169244)[journals.sagepub.com/home/cep](journals.sagepub.com/home/cep)  \nAbstract  \nIntroduction: Triggers, premonitory symptoms and physiological changes occur in the preictal migraine phase and maybe used in models for forecasting attacks. Machine learning is a promising option for such predictive analytics. The objective of this study was to explore the utility of machine learning to forecast migraine attacks based on preictal headache diary entries and simple physiological measurements.  \nMethods: In a prospective development and usability study 18 patients with migraine completed 388 headache diary entries and self-administered app-based biofeedback sessions wirelessly measuring heart rate, peripheral skin temperature and muscle tension. Several standard machine learning architectures were constructed to forecast headache the subsequent day. Models were scored with area under the receiver operating characteristics curve.  \nResults: Two-hundred-and-ninety-five days were included in the predictive modelling. The top performing model, based on random forest classification, achieved an area under the receiver operating characteristics curve of 0.62 in a hold-out partition of the dataset.  \nDiscussion: In this study we demonstrate the utility of using mobile health apps and wearables combined with machine learning to forecast headache. We argue that high-dimensional modelling may greatly improve forecasting and discuss important considerations for future design of forecasting models using machine learning and mobile health data.  \nKeywords  \nArtificial intelligence, random forest, boosting, prediction, headache, biofeedback  \nDate received: 2 December 2022; revised: 18 March 2023; accepted: 27 March 2023  \nIntroduction  \nMigraine is the leading cause for disability in the age range of 15–55 (1) and almost all sufferers experience functional impairment during the attack (2) . The disorder is for many unpredictive and the uncertainty of when a new attack occurs is associated with anxiety and further functional impairment (3) . The usual treatment strategy for migraines is to abort attacks after their occurrence or commence preventative treatmentsin order to reduce the frequency of attacks (4) . In addition, so-called preemptive treatment—in which drugs are administered specifically on days with increased risk of headache—is a promising option (5) . The notion of  \n1 Department of Neuromedicine and Movement Science, NTNU Norwegian University of Science and Technology, Trondheim, Norway 2NorHEAD, Norwegian Headache Research Centre, Norway 3Department of Psychology, NTNU Norwegian University of Science and Technology, Trondheim, Norway  \n4National Advisory Unit on Headaches, Department of Neurology and Clinical Neurophysiology, St. Olavs Hospital, Trondheim, Norway 5Department of Physical Medicine and Rehabilitation, St. Olavs Hospital, Trondheim, Norway  \n6UCL Queen Square Institute of Neurology, London, United Kingdom  \nCorresponding author:  \nAnker Stubberud, Department of Neuromedicine and Movement Science, NTNU Norwegian University of Science and Technology, Edvard Griegs gt 8, 7030, Trondheim, Norway.  \nEmail: [anker.stubberud@ntnu.no](anker.stubberud@ntnu.no)  \nCreative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License ([https://](https://)  \n[creativecommons.","cbCaifppbsJkKLO8","https://ap.wps.com/l/cbCaifppbsJkKLO8","pdf",840070,1,10,"English","en",105,"# Introduction\n## Background and rationale for forecasting migraine\n## Triggers, premonitory symptoms, and physiological changes\n## Mobile health apps and wearable data\n# Methods\n## Prospective development and usability study\n## Data collection and model construction\n## Model evaluation\n# Results\n## Predictive modeling dataset and best model performance\n# Discussion\n## Utility of combining mobile health and wearables with ML\n## Considerations for future model design","[{\"question\":\"What data sources were used to forecast migraine attacks?\",\"answer\":\"The models used preictal headache diary entries plus simple physiological measures captured wirelessly via a smartphone app and wearables, including heart rate, peripheral skin temperature, and muscle tension.\"},{\"question\":\"How was the machine-learning forecasting performance evaluated?\",\"answer\":\"Models were scored using the area under the receiver operating characteristics curve (AUC-ROC) on a hold-out partition of the dataset.\"},{\"question\":\"What were the main findings regarding model accuracy?\",\"answer\":\"Using random forest classification, the top-performing model achieved an AUC-ROC of 0.62 in the hold-out dataset, indicating moderate predictive capability for next-day headache.\"}]","Forecasting migraine with machine learning based on mobile phone diary and wearable data | 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data sources were used to forecast migraine attacks?","Question",{"text":75,"@type":76},"The models used preictal headache diary entries plus simple physiological measures captured wirelessly via a smartphone app and wearables, including heart rate, peripheral skin temperature, and muscle tension.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the machine-learning forecasting performance evaluated?",{"text":80,"@type":76},"Models were scored using the area under the receiver operating characteristics curve (AUC-ROC) on a hold-out partition of the dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the main findings regarding model accuracy?",{"text":84,"@type":76},"Using random forest classification, the top-performing model achieved an AUC-ROC of 0.62 in the hold-out dataset, indicating moderate predictive capability for next-day 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