{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T08:49:46Z","timestamp":1791622186131,"version":"4.3.4"},"reference-count":29,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["headachejournal.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Headache"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives\/Background<\/jats:title>\n                    <jats:p>Migraine is a disabling neurological disorder with substantial interindividual variability. Predicting whether an ongoing migraine attack will persist into the following day remains challenging. We evaluated the feasibility and predictive performance of personalized machine learning models for predicting next\u2010day migraine persistence using longitudinal digital headache diary data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This prospective longitudinal cohort study was designed for prognostic prediction model development. Participants were recruited from two medical centers in Taiwan between February 2023 and October 2023, and each completed a 3\u2010month digital headache diary observation period. Patients with 5\u201314 monthly headache days were included. Same\u2010day diary records from index migraine days were used to predict next\u2010day migraine persistence. Personalized and generalized models were developed using k\u2010nearest neighbors (KNN), support vector machine, random forest, and eXtreme Gradient Boosting (XGBoost). Model performance was evaluated using discrimination, calibration, and sensitivity analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 25 patients were included. Compared with the generalized KNN model, the personalized KNN model achieved significantly higher area under the curve (AUC) (0.83\u2009\u00b1\u20090.09 vs. 0.63\u2009\u00b1\u20090.04; \u0394AUC\u2009=\u20090.20). Among personalized models, KNN also outperformed support vector machine, random forest, and eXtreme gradient boosting, with \u0394AUC values of 0.23, 0.15, and 0.16, respectively; the corresponding Holm\u2010adjusted\n                      <jats:italic>p<\/jats:italic>\n                      \u2010values were &lt;0.001, 0.003, and &lt;0.001. The personalized KNN model showed the lowest Brier score (0.13\u2009\u00b1\u20090.04), with a calibration intercept of 0.08 and calibration slope of 0.80. Ablation analysis indicated that the KNN model performance was most sensitive to the removal of pain intensity, menstrual cycle status, perceived medication effectiveness, and pain location, with pain intensity showing the largest effect.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Personalized machine learning models based on digital headache diary data may feasibly predict next\u2010day persistence of ongoing migraine. Larger external validation studies are needed to confirm model generalizability and clinical applicability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/head.70178","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:57:13Z","timestamp":1784181433000},"page":"2066-2077","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Personalized machine learning prediction of next\u2010day migraine persistence using digital headache diary data"],"prefix":"10.1111","volume":"66","author":[{"given":"Ya\u2010Hsiu","family":"Tsai","sequence":"first","affiliation":[{"name":"Institute of Medical Science and Technology National Sun Yat\u2010sen University  Kaohsiung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen\u2010Lin","family":"Chiou","sequence":"additional","affiliation":[{"name":"Institute of Medical Science and Technology National Sun Yat\u2010sen University  Kaohsiung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun\u2010Jun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Neurology Kaohsiung Chang Gung Memorial Hospital  Kaohsiung Taiwan"},{"name":"College of Medicine Chang Gung University  Taoyuan Taiwan"},{"name":"Department of Information Management National Sun Yat\u2010sen University  Kaohsiung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun\u2010Ming","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Neurology Chi Mei Medical Center  Tainan Taiwan"},{"name":"School of Medicine, College of Medicine National Sun Yat\u2010sen University  Kaohsiung Taiwan"},{"name":"School of Medicine Chung Shan Medical University  Taichung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kao\u2010Chang","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Neurology Chi Mei Medical Center  Tainan Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tain\u2010Junn","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Neurology Chi Mei Medical Center  Tainan Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ja\u2010Hwung","family":"Su","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering National University of Kaohsiung  Kaohsiung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9097-1377","authenticated-orcid":false,"given":"Cheng\u2010Hsin","family":"Chuang","sequence":"additional","affiliation":[{"name":"Institute of Medical Science and Technology National Sun Yat\u2010sen University  Kaohsiung 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