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Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The climate crisis underscores the need for weather-based predictive analytics in healthcare, as weather factors contribute to ~11% of the global stroke burden. Therefore, we developed machine learning models using locoregional weather data to forecast daily acute ischemic stroke (AIS) admissions. An AIS cohort of 7914 patients admitted between 2015 and 2021 at the tertiary University Medical Center Mannheim, Germany, with a 600,000-population catchment area, was geospatially matched to German Weather Service data. Poisson regression, boosted generalized additive models, support vector machines, random forest, and extreme gradient boosting (XGB) were evaluated within a time-stratified nested cross-validation framework. XGB performed best (mean absolute error: 1.21 cases\/day). Maximum air pressure was the top predictor, with temperature exhibiting a bimodal link. Cold and heat stressor days (\n                    <jats:italic>T<\/jats:italic>\n                    <jats:sub>min_lag3<\/jats:sub>\n                    \u2009&lt;\u2009\u22122\u2009\u00b0C;\n                    <jats:italic>T<\/jats:italic>\n                    <jats:sub>perceived<\/jats:sub>\n                    \u2009&lt;\u2009\u22121.4\u2009\u00b0C;\n                    <jats:italic>T<\/jats:italic>\n                    <jats:sub>min_lag7<\/jats:sub>\n                    \u2009&gt;\u200915\u2009\u00b0C) and stormy conditions (wind gusts\u2009&gt;\u200914\u2009m\/s) increased stroke admissions. This generalizable framework could aid real-time hospital planning, effective care and forecasting of various weather-related disease burdens.\n                  <\/jats:p>","DOI":"10.1038\/s41746-025-01619-w","type":"journal-article","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T08:05:55Z","timestamp":1745568355000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine learning-based forecasting of daily acute ischemic stroke admissions using weather data"],"prefix":"10.1038","volume":"8","author":[{"given":"Nandhini","family":"Santhanam","sequence":"first","affiliation":[]},{"given":"Hee E.","family":"Kim","sequence":"additional","affiliation":[]},{"given":"David","family":"R\u00fcgamer","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Bender","sequence":"additional","affiliation":[]},{"given":"Stefan","family":"Muthers","sequence":"additional","affiliation":[]},{"given":"Chang Gyu","family":"Cho","sequence":"additional","affiliation":[]},{"given":"Angelika","family":"Alonso","sequence":"additional","affiliation":[]},{"given":"Kristina","family":"Szabo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7455-1361","authenticated-orcid":false,"given":"Franz-Simon","family":"Centner","sequence":"additional","affiliation":[]},{"given":"Holger","family":"Wenz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6864-8936","authenticated-orcid":false,"given":"Thomas","family":"Ganslandt","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Platten","sequence":"additional","affiliation":[]},{"given":"Christoph","family":"Groden","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0410-3931","authenticated-orcid":false,"given":"Michael","family":"Neumaier","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9673-5030","authenticated-orcid":false,"given":"Fabian","family":"Siegel","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1589-8699","authenticated-orcid":false,"given":"M\u00e1t\u00e9 E.","family":"Maros","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"1619_CR1","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/S1474-4422(21)00252-0","volume":"20","author":"ValeryL Feigin","year":"2021","unstructured":"Feigin, V. aleryL. et al. 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Med."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01619-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01619-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01619-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T09:03:11Z","timestamp":1745571791000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01619-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,25]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1619"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-01619-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.07.03.24309252","asserted-by":"object"}]},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,25]]},"assertion":[{"value":"9 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"M.E.M. reports unrelated consultancy to EppData GmbH and Siemens Healthineers GmbH, Germany. The remaining authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This single-center retrospective cohort study entitled \u201cWeather-based Stroke Event and Outcome Risk Modeling (WE-STORM)\u201d was approved by the local use- and access- (UAC) and ethics committees (Medical Ethics Commission II, Medical Faculty Mannheim, Heidelberg University, approval nr.: 2022-800R-MA). All methods were carried out following institutional guidelines and regulations. The ethics committee waived written informed consent due to the retrospective nature of the analyses.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}],"article-number":"225"}}