[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123470-en":3,"doc-seo-123470-105":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},123470,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",7,"Healthcare","Predicting first-time anaphylaxis in the elderly using stacked machine learning and population registries","The study develops and evaluates a stacked machine learning approach to predict first-time anaphylaxis among older adults using population registry information. It compares multiple modeling strategies and examines performance using classification metrics and calibration assessment. Feature contributions are analyzed to identify factors that most strongly support risk discrimination. The results quantify predictive accuracy on real-world data, highlighting the potential for registry-linked machine learning to support earlier identification of high-risk individuals in clinical and public-health settings.","􀀘Y􀀁E O􀀌􀀇􀀕􀀇􀀈􀀖􀀚 R􀀂􀀍􀀂􀀖􀀌􀀓􀀔  \n􀀁UBLISHE􀀝 06 N􀀐􀀋􀀂􀀃b􀀂􀀌 2025  \n􀀝OI 10 3389/􀀑􀀖􀀚􀀕􀀏 2025 1655662  \n􀀁􀀂􀀃􀀄􀀁􀀂 􀀆􀀇  \n􀀁􀀂􀀃 􀀅􀀆􀀇􀀈􀀉  \n􀀊􀀈􀀇􀀋􀀂􀀌􀀍􀀇􀀎􀀏 􀀐􀀑 􀀒􀀇􀀓􀀔􀀇􀀕􀀖􀀈􀀉 􀀊􀀈􀀇􀀎􀀂􀀗 􀀘􀀎􀀖􀀎􀀂􀀍  \n􀀈􀀁􀀉􀀃􀀁􀀊􀀁􀀂 􀀆􀀇  \n􀀘􀀖􀀌􀀖 􀀒􀀖􀀈􀀎􀀇􀀉  \n􀀊􀀈􀀇􀀋􀀂􀀌􀀍􀀇􀀎􀀏 􀀐􀀑 􀀒􀀂􀀍􀀍􀀇􀀈􀀖􀀉 􀀙􀀎􀀖􀀚􀀏􀀁􀀔􀀖􀀌􀀚􀀂􀀍 􀀘􀀓􀀔􀀛􀀚􀀂􀀌􀀉  \n􀀊􀀈􀀇􀀋􀀂􀀌􀀍􀀇􀀎􀀏 􀀐􀀑 􀀒􀀇􀀓􀀔􀀇􀀕􀀖􀀈􀀉 􀀊􀀈􀀇􀀎􀀂􀀗 􀀘􀀎􀀖􀀎􀀂􀀍  \n􀀜􀀋􀀌􀀈􀀈􀀁􀀍􀀎􀀌􀀏􀀂􀀁􀀏􀀋􀀁  \n􀀝􀀐􀀈􀀇 􀀒􀀐􀀌􀀖  \n 􀀎􀀃􀀐􀀌􀀖􀀞􀀛􀀇􀀓 􀀂􀀍  \n􀀈􀀁􀀋􀀁􀀃􀀉􀀁􀀂 28 J􀀛􀀈􀀂 2025  \n􀀐􀀋􀀋􀀁􀀎􀀄􀀁􀀂 10 O􀀓􀀎􀀐b􀀂􀀌 2025  \n􀀎􀀑􀀆􀀒􀀃􀀍􀀓􀀁􀀂 06 N􀀐􀀋􀀂􀀃b􀀂􀀌 2025  \n􀀋􀀃􀀄􀀐􀀄􀀃􀀌􀀏  \n􀀒􀀐􀀌􀀖 􀀝􀀉 R􀀐􀀓􀀔􀀂 D 􀀖􀀈􀀗 􀀒􀀛ñ􀀐z-􀀁􀀖􀀈􀀐 R (2025) P􀀌􀀂􀀗􀀇􀀓􀀎􀀇􀀈􀀕 fi􀀌􀀍􀀎-􀀎􀀇􀀃􀀂 􀀖􀀈􀀖p􀀔􀀏􀀚􀀖x􀀇􀀍 􀀇􀀈 􀀎􀀔􀀂 􀀂􀀚􀀗􀀂􀀌􀀚􀀏􀀛􀀍􀀇􀀈􀀕 􀀍􀀎􀀖􀀓􀀆􀀂􀀗 􀀃􀀖􀀓􀀔􀀇􀀈􀀂 􀀚􀀂􀀖􀀌􀀈􀀇􀀈􀀕 􀀖􀀈􀀗 p􀀐p􀀛􀀚􀀖􀀎􀀇􀀐􀀈 􀀌􀀂􀀕􀀇􀀍􀀎􀀂􀀌􀀍  \nF􀀌􀀐􀀈􀀎 􀀅􀀚􀀚􀀂􀀌􀀕􀀏 6:1655662  \n􀀗􀀐􀀇: 10 3389/􀀑􀀖􀀚􀀕􀀏 2025 1655662  \n􀀋􀀌􀀎􀀇􀀈􀀃􀀔􀀓􀀄  \n© 2025 􀀒􀀐􀀌􀀖􀀉 R􀀐􀀓􀀔􀀂 􀀖􀀈􀀗 􀀒􀀛ñ􀀐z-􀀁􀀖􀀈􀀐 􀀝􀀔􀀇􀀍 􀀇􀀍􀀖􀀈 􀀐p􀀂􀀈-􀀖􀀓􀀓􀀂􀀍􀀍 􀀖􀀌􀀎􀀇􀀓􀀚􀀂 􀀗􀀇􀀍􀀎􀀌􀀇b􀀛􀀎􀀂􀀗 􀀛􀀈􀀗􀀂􀀌 􀀎􀀔􀀂􀀎􀀂􀀌􀀃􀀍 􀀐􀀑 􀀎􀀔􀀂 􀀁􀀌􀀂􀀖􀀎􀀇􀀋􀀂 􀀁􀀐􀀃􀀃􀀐􀀈􀀍 􀀅􀀎􀀎􀀌􀀇b􀀛􀀎􀀇􀀐􀀈 L􀀇􀀓􀀂􀀈􀀍􀀂 (􀀁􀀁 BY) 􀀝􀀔􀀂 􀀛􀀍􀀂􀀉 􀀗􀀇􀀍􀀎􀀌􀀇b􀀛􀀎􀀇􀀐􀀈 􀀐􀀌􀀌􀀂p􀀌􀀐􀀗􀀛􀀓􀀎􀀇􀀐􀀈 􀀇􀀈 􀀐􀀎􀀔􀀂􀀌 􀀑􀀐􀀌􀀛􀀃􀀍 􀀇􀀍 p􀀂􀀌􀀃􀀇􀀎􀀎􀀂􀀗􀀉 p􀀌􀀐􀀋􀀇􀀗􀀂􀀗 􀀎􀀔􀀂 􀀐􀀌􀀇􀀕􀀇􀀈􀀖􀀚 􀀖􀀛􀀎􀀔􀀐􀀌(􀀍) 􀀖􀀈􀀗 􀀎􀀔􀀂􀀓􀀐p􀀏􀀌􀀇􀀕􀀔􀀎 􀀐w􀀈􀀂􀀌(􀀍) 􀀖􀀌􀀂 􀀓􀀌􀀂􀀗􀀇􀀎􀀂􀀗 􀀖􀀈􀀗 􀀎􀀔􀀖􀀎 􀀎􀀔􀀂􀀐􀀌􀀇􀀕􀀇􀀈􀀖􀀚 p􀀛b􀀚􀀇􀀓􀀖􀀎􀀇􀀐􀀈 􀀇􀀈 􀀎􀀔􀀇􀀍 j􀀐􀀛􀀌􀀈􀀖􀀚 􀀇􀀍 􀀓􀀇􀀎􀀂􀀗􀀉 􀀇􀀈􀀖􀀓􀀓􀀐􀀌􀀗􀀖􀀈􀀓􀀂 w􀀇􀀎􀀔 􀀖􀀓􀀓􀀂p􀀎􀀂􀀗 􀀖􀀓􀀖􀀗􀀂􀀃􀀇􀀓 p􀀌􀀖􀀓􀀎􀀇􀀓􀀂 N􀀐 􀀛􀀍􀀂􀀉 􀀗􀀇􀀍􀀎􀀌􀀇b􀀛􀀎􀀇􀀐􀀈 􀀐􀀌 􀀌􀀂p􀀌􀀐􀀗􀀛􀀓􀀎􀀇􀀐􀀈 􀀇􀀍 p􀀂􀀌􀀃􀀇􀀎􀀎􀀂􀀗 w􀀔􀀇􀀓􀀔 􀀗􀀐􀀂􀀍 􀀈􀀐􀀎 􀀓􀀐􀀃p􀀚􀀏 w􀀇􀀎􀀔􀀎􀀔􀀂􀀍􀀂 􀀎􀀂􀀌􀀃􀀍  \n􀀁􀀂􀀃􀀄􀀅􀀆􀀇􀀅􀀈􀀉 􀀋􀀂􀀌􀀇􀀍􀀇􀀅􀀎􀀃 􀀏􀀈􀀏􀀐􀀑􀀒􀀓􀀏􀀔􀀅􀀌􀀅􀀈 􀀇􀀑􀀃 􀀃􀀓􀀄􀀃􀀂􀀓􀀒 􀀕􀀌􀀅􀀈􀀉 􀀌􀀇􀀏􀀆􀀖􀀃􀀄􀀎􀀏􀀆􀀑􀀅􀀈􀀃 􀀓􀀃􀀏􀀂􀀈􀀅􀀈􀀉 􀀏􀀈􀀄􀀐􀀗􀀐􀀕􀀓􀀏􀀇􀀅􀀗􀀈 􀀂􀀃􀀉􀀅􀀌􀀇􀀃􀀂􀀌  \n􀀘􀀗􀀈􀀅 􀀙􀀗􀀂􀀏 􀀚􀀛􀀜 􀀝􀀏􀀞􀀅􀀄 􀀗􀀆􀀑􀀃􀀚 􀀏􀀈􀀄 􀀗􀀌􀀏 􀀙􀀕ñ􀀗z􀀍C􀀏􀀈􀀗 2􀀜3􀀜4  \n1 R􀀂􀀍􀀂􀀖􀀌􀀓􀀔 􀀙􀀈􀀍􀀎􀀇􀀎􀀛􀀎􀀂 􀀑􀀐􀀌 E􀀋􀀖􀀚􀀛􀀖􀀎􀀇􀀐􀀈 􀀖􀀈􀀗 P􀀛b􀀚􀀇􀀓 P􀀐􀀚􀀇􀀓􀀇􀀂􀀍 (􀀙R􀀅PP)􀀉 􀀊􀀈􀀇􀀋􀀂􀀌􀀍􀀇􀀎􀀖􀀎 􀀙􀀈􀀎􀀂􀀌􀀈􀀖􀀓􀀇􀀐􀀈􀀖􀀚 􀀗􀀂 􀀁􀀖􀀎􀀖􀀚􀀛􀀈􀀏􀀖(􀀊􀀙􀀁)􀀉 B􀀖􀀌􀀓􀀂􀀚􀀐􀀈􀀖􀀉 􀀘p􀀖􀀇􀀈􀀉 2􀀅􀀚􀀚􀀂􀀌􀀕􀀏 D􀀂p􀀖􀀌􀀎􀀃􀀂􀀈􀀎􀀉 H􀀐􀀍p􀀇􀀎􀀖􀀚 􀀁􀀚􀀇􀀈􀀇􀀓􀀉 B􀀖􀀌􀀓􀀂􀀚􀀐􀀈􀀖􀀉 􀀘p􀀖􀀇􀀈􀀉 3􀀒􀀂􀀝R􀀙 2􀀅—􀀙􀀈􀀍􀀎􀀇􀀎􀀛􀀎􀀗’􀀙􀀈􀀋􀀂􀀍􀀎􀀇􀀕􀀖􀀓􀀇􀀐􀀈􀀍 B􀀇􀀐􀀃è􀀗􀀇q􀀛􀀂􀀍 􀀅􀀛􀀕􀀛􀀍􀀎 P􀀇 􀀙 􀀘􀀛􀀈􀀏􀀂􀀌 (􀀙D􀀙B􀀅P􀀘)􀀉 B􀀖􀀌􀀓􀀂􀀚􀀐􀀈􀀖􀀉 􀀘p􀀖􀀇􀀈􀀉 4R􀀙􀀁OR􀀘—RE􀀙-􀀙􀀈􀀍􀀎􀀇􀀎􀀛􀀎􀀐􀀗􀀂 􀀘􀀖􀀚􀀛􀀗 􀀁􀀖􀀌􀀚􀀐􀀍 􀀙􀀙􀀙􀀉 􀀒􀀖􀀗􀀌􀀇􀀗􀀉 􀀘p􀀖􀀇􀀈  \n􀀆􀀕􀀖􀀗􀀘􀀙􀀚􀀛􀀜􀀝􀀞 􀀅􀀈􀀖p􀀔􀀏􀀚􀀖x􀀇􀀍 􀀇􀀍 􀀖 􀀍􀀂􀀋􀀂􀀌􀀂􀀉 p􀀐􀀎􀀂􀀈􀀎􀀇􀀖􀀚􀀚􀀏 􀀚􀀇􀀑􀀂-􀀎􀀔􀀌􀀂􀀖􀀎􀀂􀀈􀀇􀀈􀀕 􀀖􀀚􀀚􀀂􀀌􀀕􀀇􀀓􀀌􀀂􀀖􀀓􀀎􀀇􀀐􀀈 􀀎􀀔􀀖􀀎 􀀌􀀂q􀀛􀀇􀀌􀀂􀀍 􀀌􀀖p􀀇􀀗 􀀇􀀗􀀂􀀈􀀎􀀇fi􀀓􀀖􀀎􀀇􀀐􀀈 􀀖􀀈􀀗 􀀇􀀈􀀎􀀂􀀌􀀋􀀂􀀈􀀎􀀇􀀐􀀈 P􀀌􀀂􀀗􀀇􀀓􀀎􀀇􀀈􀀕􀀇􀀈􀀗􀀇􀀋􀀇􀀗􀀛􀀖􀀚􀀍 􀀖􀀎 􀀌􀀇􀀍􀀆 􀀌􀀂􀀃􀀖􀀇􀀈􀀍 􀀖 􀀓􀀚􀀇􀀈􀀇􀀓􀀖􀀚 􀀓􀀔􀀖􀀚􀀚􀀂􀀈􀀕􀀂 􀀗􀀛􀀂 􀀎􀀐 􀀇􀀎􀀍 􀀃􀀛􀀚􀀎􀀇􀀑􀀖􀀓􀀎􀀐􀀌􀀇􀀖􀀚 􀀈􀀖􀀎􀀛􀀌􀀂􀀖􀀈􀀗 􀀋􀀖􀀌􀀇􀀖b􀀚􀀂 p􀀌􀀂􀀍􀀂􀀈􀀎􀀖􀀎􀀇􀀐􀀈  \n􀀌 je􀀖tive􀀞 􀀝􀀐 􀀗􀀂􀀋􀀂􀀚􀀐p 􀀖􀀈􀀗 􀀂􀀋􀀖􀀚􀀛􀀖􀀎􀀂 􀀂xp􀀚􀀖􀀇􀀈􀀖b􀀚􀀂 􀀃􀀖􀀓􀀔􀀇􀀈􀀂 􀀚􀀂􀀖􀀌􀀈􀀇􀀈􀀕 􀀃􀀐􀀗􀀂􀀚􀀍 􀀎􀀔􀀖􀀎 p􀀌􀀂􀀗􀀇􀀓􀀎 􀀎􀀔􀀂 􀀌􀀇􀀍􀀆 􀀐􀀑 􀀖􀀈􀀖p􀀔􀀏􀀚􀀖x􀀇􀀍 􀀛􀀍􀀇􀀈􀀕 􀀌􀀐􀀛􀀎􀀇􀀈􀀂􀀚􀀏 􀀓􀀐􀀚􀀚􀀂􀀓􀀎􀀂􀀗 􀀓􀀚􀀇􀀈􀀇􀀓􀀖􀀚 􀀗􀀖􀀎􀀖 Meth􀀚􀀝s􀀞 W􀀂 􀀖􀀈􀀖􀀚􀀏􀀍􀀂􀀗 􀀖 􀀃􀀖􀀎􀀓􀀔􀀂􀀗 􀀓􀀖􀀍􀀂-􀀓􀀐􀀈􀀎􀀌􀀐􀀚 􀀗􀀖􀀎􀀖􀀍􀀂􀀎 􀀗􀀂􀀌􀀇􀀋􀀂􀀗 􀀑􀀌􀀐􀀃􀀖􀀈􀀐􀀈􀀏􀀃􀀇􀀍􀀂􀀗 􀀂􀀚􀀂􀀓􀀎􀀌􀀐􀀈􀀇􀀓 􀀔􀀂􀀖􀀚􀀎􀀔 􀀌􀀂􀀓􀀐􀀌􀀗􀀍 􀀅􀀑􀀎􀀂􀀌 􀀖pp􀀚􀀏􀀇􀀈􀀕 􀀓􀀔􀀇-􀀍q􀀛􀀖􀀌􀀂􀀗-b􀀖􀀍􀀂􀀗􀀑􀀂􀀖􀀎􀀛􀀌􀀂 􀀍􀀂􀀚􀀂􀀓􀀎􀀇􀀐􀀈􀀉 w􀀂 􀀎􀀌􀀖􀀇􀀈􀀂􀀗 􀀃􀀛􀀚􀀎􀀇p􀀚􀀂 􀀓􀀚􀀖􀀍􀀍􀀇fi􀀓􀀖􀀎􀀇􀀐􀀈 􀀖􀀚􀀕􀀐􀀌􀀇􀀎􀀔􀀃􀀍—􀀇􀀈􀀓􀀚􀀛􀀗􀀇􀀈􀀕􀀚􀀐􀀕􀀇􀀍􀀎􀀇􀀓 􀀌􀀂􀀕􀀌􀀂􀀍􀀍􀀇􀀐􀀈􀀉 􀀗􀀂􀀓􀀇􀀍􀀇􀀐􀀈 􀀎􀀌􀀂􀀂􀀍􀀉 􀀌􀀖􀀈􀀗􀀐􀀃 􀀑􀀐􀀌􀀂􀀍􀀎􀀍􀀉 XGB􀀐􀀐􀀍􀀎􀀉 􀀖􀀈􀀗 􀀖 􀀍􀀎􀀖􀀓􀀆􀀇􀀈􀀕􀀂􀀈􀀍􀀂􀀃b􀀚􀀂 􀀒􀀐􀀗􀀂􀀚 p􀀂􀀌􀀑􀀐􀀌􀀃􀀖􀀈􀀓􀀂 w􀀖􀀍 􀀂􀀋􀀖􀀚􀀛􀀖􀀎􀀂􀀗 􀀛􀀍􀀇􀀈􀀕 􀀅􀀊􀀁􀀉 􀀍􀀂􀀈􀀍􀀇􀀎􀀇􀀋􀀇􀀎􀀏􀀉 􀀍p􀀂􀀓􀀇fi􀀓􀀇􀀎􀀏􀀉 p􀀌􀀂􀀓􀀇􀀍􀀇􀀐􀀈􀀉 􀀖􀀈􀀗 F1-􀀍􀀓􀀐􀀌􀀂 􀀘H􀀅P 􀀋􀀖􀀚􀀛􀀂􀀍 w􀀂􀀌􀀂 􀀛􀀍􀀂􀀗 􀀎􀀐 􀀖􀀍􀀍􀀂􀀍􀀍 􀀃􀀐􀀗􀀂􀀚 􀀂xp􀀚􀀖􀀇􀀈􀀖b􀀇􀀚􀀇􀀎􀀏  \n􀀈es􀀛lts􀀞 􀀝􀀔􀀂 b􀀂􀀍􀀎-p􀀂􀀌􀀑􀀐􀀌􀀃􀀇􀀈􀀕 􀀃􀀐􀀗􀀂􀀚 􀀖􀀓􀀔􀀇􀀂􀀋􀀂􀀗 􀀖􀀈 􀀅􀀊􀀁 􀀐􀀑 0 79􀀉 􀀗􀀂􀀃􀀐􀀈􀀍􀀎􀀌􀀖􀀎􀀇􀀈􀀕􀀔􀀇􀀕􀀔 􀀗􀀇􀀍􀀓􀀌􀀇􀀃􀀇􀀈􀀖􀀎􀀇􀀐􀀈 􀀖􀀈􀀗 b􀀖􀀚􀀖􀀈􀀓􀀂􀀗 􀀍􀀂􀀈􀀍􀀇􀀎􀀇􀀋􀀇􀀎􀀏/􀀍p􀀂􀀓􀀇fi􀀓􀀇􀀎􀀏 K􀀂􀀏 p􀀌􀀂􀀗􀀇􀀓􀀎􀀐􀀌􀀍􀀇􀀈􀀓􀀚􀀛􀀗􀀂􀀗 􀀔􀀂􀀖􀀚􀀎􀀔􀀓􀀖􀀌􀀂 􀀛􀀎􀀇􀀚􀀇􀀍􀀖􀀎􀀇􀀐􀀈 p􀀖􀀎􀀎􀀂􀀌􀀈􀀍􀀉 􀀖􀀕􀀂􀀉 􀀍􀀐􀀓􀀇􀀐􀀂􀀓􀀐􀀈􀀐􀀃􀀇􀀓 p􀀌􀀐x􀀏(􀀓􀀐p􀀖􀀏􀀃􀀂􀀈􀀎 􀀚􀀂􀀋􀀂􀀚)􀀉 􀀖􀀈􀀗 􀀍p􀀂􀀓􀀇fi􀀓 􀀗􀀇􀀖􀀕􀀈􀀐􀀍􀀎􀀇􀀓 􀀓􀀐􀀗􀀂􀀍 􀀌􀀂􀀚􀀖􀀎􀀂􀀗 􀀎􀀐 􀀖􀀚􀀚􀀂􀀌􀀕􀀇􀀓 􀀓􀀐􀀈􀀗􀀇􀀎􀀇􀀐􀀈􀀍􀀋􀀚􀀜􀀖l􀀛si􀀚􀀜􀀞 􀀝􀀔􀀇􀀍 􀀍􀀎􀀛􀀗􀀏 􀀗􀀂􀀃􀀐􀀈􀀍􀀎􀀌􀀖􀀎􀀂􀀍 􀀎􀀔􀀂 p􀀐􀀎􀀂􀀈􀀎􀀇􀀖􀀚 􀀐􀀑 􀀇􀀈􀀎􀀂􀀌p􀀌􀀂􀀎􀀖b􀀚􀀂 􀀃􀀖􀀓􀀔􀀇􀀈􀀂􀀚􀀂􀀖􀀌􀀈􀀇􀀈􀀕 􀀖pp􀀌􀀐􀀖􀀓􀀔􀀂􀀍 􀀎􀀐 􀀍􀀛pp􀀐􀀌􀀎 􀀎􀀔􀀂 􀀂􀀖􀀌􀀚􀀏 􀀇􀀗􀀂􀀈􀀎􀀇fi􀀓􀀖􀀎􀀇􀀐􀀈 􀀐􀀑 􀀇􀀈􀀗􀀇􀀋􀀇􀀗􀀛􀀖􀀚􀀍 􀀖􀀎 􀀔􀀇􀀕􀀔􀀌􀀇􀀍􀀆 􀀐􀀑 􀀖􀀈􀀖p􀀔􀀏􀀚􀀖x􀀇􀀍 􀀝􀀔􀀂􀀍􀀂 􀀎􀀐􀀐􀀚􀀍 􀀓􀀖􀀈 􀀂􀀈􀀔􀀖􀀈􀀓􀀂 􀀓􀀚􀀇􀀈􀀇􀀓􀀖􀀚 􀀌􀀇􀀍􀀆 􀀍􀀎􀀌􀀖􀀎􀀇fi􀀓􀀖􀀎􀀇􀀐􀀈 􀀖􀀈􀀗􀀇􀀈􀀑􀀐􀀌􀀃 p􀀌􀀂􀀋􀀂􀀈􀀎􀀇􀀋􀀂 􀀍􀀎􀀌􀀖􀀎􀀂􀀕􀀇􀀂􀀍 􀀇􀀈 􀀌􀀐􀀛􀀎􀀇􀀈􀀂 p􀀌􀀖􀀓􀀎􀀇􀀓􀀂  \nK􀀁􀀇􀀊􀀌􀀈􀀂􀀍  \n􀀕􀀜􀀕phyl􀀕xis p􀀙e􀀝i􀀖ti􀀚􀀜, st􀀕􀀖􀀗e􀀝 m􀀕􀀖hi􀀜e le􀀕􀀙􀀜i􀀜􀀘 m􀀚􀀝el, 􀀕􀀝mi􀀜ist􀀙􀀕tive he􀀕lth􀀖􀀕􀀙e 􀀝􀀕t􀀕, el􀀝e􀀙ly p􀀚p􀀛l􀀕ti􀀚􀀜, he􀀕lth􀀖􀀕􀀙e 􀀛tilis􀀕ti􀀚􀀜 p􀀕tte􀀙􀀜s, 􀀕􀀙tifi􀀖i􀀕l i􀀜telli􀀘e􀀜􀀖e, 􀀕lle􀀙􀀘y 􀀙is􀀗 st􀀙􀀕tifi􀀖􀀕ti􀀚􀀜  \nI􀀈􀀇􀀂􀀗􀀄􀀕􀀆􀀇􀀅􀀗􀀈  \n􀀁􀀂􀀃􀀄􀀅􀀆􀀇􀀃􀀈􀀉􀀊 􀀉􀀊 􀀃 􀀊􀀌􀀍􀀌􀀎􀀌 􀀃􀀂􀀏 􀀄􀀐􀀑􀀌􀀂􀀑􀀉􀀃􀀇􀀇􀀆 􀀇􀀉􀀒􀀌􀀓􀀑􀀅􀀎􀀌􀀃􀀑􀀌􀀂􀀉􀀂􀀔 􀀃􀀇􀀇􀀌􀀎􀀔􀀉􀀕 􀀎􀀌􀀃􀀕􀀑􀀉􀀐􀀂 􀀑􀀅􀀃􀀑 􀀕􀀃􀀂􀀐􀀕􀀕􀀖􀀎 􀀎􀀃􀀄􀀉􀀏􀀇􀀆 􀀃􀀂􀀏 􀀏􀀌􀀗􀀃􀀂􀀏􀀊 􀀉􀀗􀀗􀀌􀀏􀀉􀀃􀀑􀀌 􀀗􀀌􀀏􀀉􀀕􀀃􀀇 􀀉􀀂􀀑􀀌􀀎􀀍􀀌􀀂􀀑􀀉􀀐􀀂􀀘 􀀙􀀌􀀊􀀄􀀉􀀑􀀌 􀀉􀀑􀀊 􀀕􀀇􀀉􀀂􀀉􀀕􀀃􀀇􀀖􀀎􀀔􀀌􀀂􀀕􀀆􀀚 􀀄􀀎􀀌􀀏􀀉􀀕􀀑􀀉􀀂􀀔 􀀑􀀅􀀌 􀀐􀀂􀀊􀀌􀀑 􀀐􀀒 􀀛􀀎􀀊􀀑􀀓􀀑􀀉􀀗􀀌 􀀃􀀂􀀃􀀄􀀅􀀆􀀇􀀃􀀈􀀉􀀊 􀀜􀀑􀀅􀀌 􀀛􀀎􀀊􀀑 􀀎􀀌􀀕􀀐􀀎􀀏􀀌􀀏 􀀏􀀉􀀃􀀔􀀂􀀐􀀊􀀉􀀊 􀀐􀀒􀀃􀀂􀀃􀀄􀀅􀀆􀀇􀀃􀀈􀀉􀀊􀀝 􀀎􀀌􀀗􀀃􀀉􀀂􀀊 􀀃 􀀊􀀉􀀔􀀂􀀉􀀛􀀕􀀃􀀂􀀑 􀀕􀀅􀀃􀀇􀀇􀀌􀀂􀀔􀀌􀀘 􀀞􀀅􀀉􀀊 􀀏􀀉􀀒􀀛􀀕􀀖􀀇􀀑􀀆 􀀃􀀎􀀉􀀊􀀌􀀊 􀀒􀀎􀀐􀀗 􀀑􀀅􀀌 􀀕􀀐􀀗􀀄􀀇􀀌􀀈􀀉􀀂􀀑􀀌􀀎􀀄􀀇􀀃􀀆 􀀐􀀒 􀀉􀀂􀀏􀀉􀀍􀀉􀀏􀀖􀀃􀀇 􀀎􀀉􀀊 􀀒􀀃􀀕􀀑􀀐􀀎􀀊􀀚 􀀉􀀂􀀕􀀇􀀖􀀏􀀉􀀂􀀔 􀀖􀀂􀀏􀀌􀀎􀀇􀀆􀀉􀀂􀀔 􀀅􀀌􀀃􀀇􀀑􀀅 􀀕􀀐􀀂􀀏􀀉􀀑􀀉􀀐􀀂􀀊􀀚􀀄􀀐􀀇􀀆􀀄􀀅􀀃􀀎􀀗􀀃􀀕􀀆􀀚 􀀃􀀂􀀏 􀀊􀀐􀀕􀀉􀀐􀀌􀀕􀀐􀀂􀀐􀀗􀀉􀀕 􀀏􀀌􀀑􀀌􀀎􀀗􀀉􀀂􀀃􀀂􀀑􀀊􀀚 w􀀅􀀉􀀕􀀅 􀀃􀀎􀀌 􀀐􀀒􀀑􀀌􀀂 􀀂􀀐􀀑 􀀃􀀏􀀌q􀀖􀀃􀀑􀀌􀀇􀀆􀀕􀀃􀀄􀀑􀀖􀀎􀀌􀀏 􀀉􀀂 􀀕􀀖􀀎􀀎􀀌􀀂􀀑 􀀊􀀕􀀎􀀌􀀌􀀂􀀉􀀂􀀔 􀀄􀀎􀀐􀀑􀀐􀀕􀀐􀀇􀀊 􀀜1􀀚 2􀀝􀀘  \nI􀀂 􀀗􀀉􀀏􀀏􀀇􀀌􀀓􀀃􀀔􀀌􀀏 􀀃􀀂􀀏 􀀌􀀇􀀏􀀌􀀎􀀇􀀆 􀀄􀀐􀀄􀀖􀀇􀀃􀀑􀀉􀀐􀀂􀀊􀀚 􀀃􀀂􀀃􀀄􀀅􀀆􀀇􀀃􀀈􀀉􀀊 􀀉􀀊 􀀐􀀒􀀑􀀌􀀂 􀀗􀀐􀀎􀀌 􀀊􀀌􀀍􀀌􀀎􀀌 􀀃􀀂􀀏􀀃􀀊􀀊􀀐􀀕􀀉􀀃􀀑􀀌􀀏 w􀀉􀀑􀀅 􀀅􀀉􀀔􀀅􀀌􀀎 􀀅􀀐􀀊􀀄􀀉􀀑􀀃􀀇􀀉􀀊􀀃􀀑􀀉􀀐􀀂 􀀎􀀃􀀑􀀌􀀊 􀀕􀀐􀀗􀀄􀀃􀀎􀀌􀀏 􀀑􀀐 􀀆􀀐􀀖􀀂􀀔􀀌􀀎 􀀔􀀎􀀐􀀖􀀄􀀊􀀘 I􀀂􀀊􀀌􀀕􀀑  \nF􀀂􀀗􀀈􀀇􀀅􀀃􀀂􀀌 􀀅􀀈 A􀀓􀀓􀀃􀀂􀀉􀀒 0􀀚 f􀀂􀀗􀀈􀀇􀀅􀀃􀀂􀀌􀀅􀀈 .􀀗􀀂􀀉  \n􀀍􀀌􀀂􀀐􀀗􀀊 􀀃􀀂􀀏 􀀗􀀌􀀏􀀉􀀕􀀃􀀑􀀉􀀐􀀂􀀊􀀚 􀀄􀀃􀀎􀀑􀀉􀀕􀀖􀀇􀀃􀀎􀀇􀀆 􀀃􀀂􀀃􀀇􀀔􀀌􀀊􀀉􀀕􀀊 􀀃􀀂􀀏 􀀃􀀂􀀑􀀉","cbCaifAZ3D3LFpe6","https://ap.wps.com/l/cbCaifAZ3D3LFpe6","pdf",759596,1,9,"English","en",105,"# Methods\n## Data sources and population registries\n## Stacked machine learning approach\n# Results\n## Model performance and evaluation\n## Feature importance and risk factors\n# Discussion\n## Clinical and public-health implications","[{\"question\":\"What is the document’s main goal?\",\"answer\":\"To predict first-time anaphylaxis in elderly people using a stacked machine learning method trained on population registry data.\"},{\"question\":\"What type of data is used to build the prediction models?\",\"answer\":\"Information drawn from population registries, linked to individuals to create model features for risk estimation.\"},{\"question\":\"How is the model evaluated in the study?\",\"answer\":\"Performance is assessed using standard predictive evaluation metrics and related analyses, including inspection of which factors contribute most to prediction.\"}]","Predicting first-time anaphylaxis in the elderly using stacked machine learning and population registries | 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