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In this study, we enrolled 126 patients diagnosed with LA\u2010HLH and 254 with non\u2010LA\u2010HLH. A machine learning\u2010based predictive model was developed and validated to enable timely differentiation of LA\u2010HLH from other HLH subtypes. The model incorporated 11 predictive variables for early LA\u2010HLH prediction, among which the top five most influential were age, ferritin, monocyte percentage, haemoglobin and platelet count. Among seven machine learning algorithms evaluated, the random forest model demonstrated the best performance, achieving an area under the curve (AUC) of 0.946 on the training set and 0.794 on the validation set. We subsequently evaluated combined models that incorporated disease\u2010specific indicators such as soluble interleukin\u20102 receptor (sCD25) and PET\u2010CT SUVmax, which resulted in a non\u2010significant increase in AUC on the validation set. Finally, the optimal model was deployed as a web\u2010based tool to support early aetiological differentiation. This may facilitate prompt initiation of targeted examination and appropriate treatment for patients with LA\u2010HLH.<\/jats:p>","DOI":"10.1111\/bjh.70447","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:51:47Z","timestamp":1774313507000},"page":"2047-2058","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Early prediction of adult lymphoma\u2010associated haemophagocytic lymphohistiocytosis using an interpretable machine learning model"],"prefix":"10.1111","volume":"208","author":[{"given":"Mengfan","family":"Luan","sequence":"first","affiliation":[{"name":"Department of Hematology Qilu Hospital of Shandong University  Jinan Shandong China"},{"name":"Shandong Key Laboratory of Hematological Diseases and Immune Microenvironment Qilu Hospital of Shandong University  Jinan Shandong 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