{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:56:28Z","timestamp":1772499388609,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T00:00:00Z","timestamp":1761350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Healthcare systems worldwide have faced unprecedented pressure during crises such as the COVID-19 pandemic, exposing limits in managing scarce hospital resources. Many predictive models remain static, unable to adapt to new variants, shifting conditions, or diverse patient populations. This work proposes a dynamic prioritization framework that recalculates severity scores in batch mode when new factors appear and applies them instantly through a streaming pipeline to incoming patients. Unlike approaches focused only on fixed mortality or severity risks, our model integrates dual datasets (survivors and non-survivors) to refine feature selection and weighting, enhancing robustness. Built on a big data infrastructure (Spark\/Databricks), it ensures scalability and responsiveness, even with millions of records. Experimental results confirm the effectiveness of this architecture: The artificial neural network (ANN) achieved 98.7% accuracy, with higher precision and recall than traditional models, while random forest and logistic regression also showed strong AUC values. Additional tests, including temporal validation and real-time latency simulation, demonstrated both stability over time and feasibility for deployment in near-real-world conditions. By combining adaptability, robustness, and scalability, the proposed framework offers a methodological contribution to healthcare analytics, supporting fair and effective hospitalization prioritization during pandemics and other public health emergencies.<\/jats:p>","DOI":"10.3390\/bdcc9110271","type":"journal-article","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T07:31:58Z","timestamp":1761550318000},"page":"271","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Scalable Predictive Modeling for Hospitalization Prioritization: A Hybrid Batch\u2013Streaming Approach"],"prefix":"10.3390","volume":"9","author":[{"given":"Nisrine","family":"Berros","sequence":"first","affiliation":[{"name":"Engineering Sciences Laboratory, National School of Applied Sciences, Ibn Tofail University, Kenitra 14000, Morocco"}]},{"given":"Youness","family":"Filaly","sequence":"additional","affiliation":[{"name":"Engineering Sciences Laboratory, National School of Applied Sciences, Ibn Tofail University, Kenitra 14000, Morocco"}]},{"given":"Fatna","family":"El Mendili","sequence":"additional","affiliation":[{"name":"School of Technology, Moulay Ismail University Meknes, Meknes 50050, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4018-437X","authenticated-orcid":false,"given":"Younes","family":"El Bouzekri El Idrissi","sequence":"additional","affiliation":[{"name":"Engineering Sciences Laboratory, National School of Applied Sciences, Ibn Tofail University, Kenitra 14000, Morocco"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Filip, R., Gheorghita Puscaselu, R., Anchidin-Norocel, L., Dimian, M., and Savage, W.K. 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