[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126377-en":3,"doc-seo-126377-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126377,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",7,"Healthcare","Accurate prediction of sepsis from pediatric emergency department to PICU using a machine-learning model","Accurate prediction of sepsis in children relies on early identification from emergency department presentation to subsequent PICU admission. This work evaluates an electronic health record–driven machine-learning approach to estimate sepsis risk and compares performance across models and feature sets. Results report discrimination metrics, calibration behavior, and subgroup analyses using pediatric variables, supporting an early warning pathway for timely escalation of care to the pediatric intensive care unit.","TY􀀓E O􀀌􀀔􀀙􀀔􀀓􀀅􀀐 􀀚􀀑􀀆􀀑􀀅􀀌c􀀂􀀓􀀖BL􀀔􀀛HED 10 Oc􀀖􀀃b􀀑􀀌 2025 DO􀀔 10􀀉3389/f􀀝􀀑􀀗􀀉2025􀀉1610187  \n􀀁􀀂􀀃􀀄􀀁􀀂 􀀆􀀇  \n􀀁􀀂􀀃􀀄􀀅􀀆 􀀈􀀉 􀀊􀀋􀀌􀀌􀀅􀀍􀀎  \n􀀏􀀅􀀐􀀑 􀀒􀀓􀀔􀀕􀀑􀀌􀀆􀀔􀀖􀀍􀀎 􀀒􀀓􀀔􀀖􀀑􀀗 􀀈􀀖􀀅􀀖􀀑􀀆  \n􀀈􀀁􀀉􀀃􀀁􀀊􀀁􀀂 􀀆􀀇  \n􀀘􀀂􀀃􀀓􀀙􀀂􀀑􀀓􀀙 􀀘􀀂􀀅􀀓􀀙􀀎  \n􀀈􀀔􀀌 􀀚􀀋􀀓 􀀚􀀋􀀓 􀀈􀀂􀀅􀀛 􀀜􀀃􀀆􀀝􀀔􀀖􀀅􀀐􀀎 􀀞􀀂􀀔􀀓􀀅􀀊􀀅􀀌􀀖 􀀓 􀀊􀀅􀀓􀀋􀀑􀀐 L􀀑􀀗􀀑􀀆􀀄􀀅􀀎  \n􀀞ONI􀀞E􀀁 I􀀓􀀆􀀖􀀔􀀖􀀋􀀖􀀑 􀀃f Ex􀀝􀀑􀀌􀀔􀀄􀀑􀀓􀀖􀀅􀀐 􀀊􀀑􀀗􀀔c􀀔􀀓􀀑􀀎 N􀀅􀀖􀀔􀀃􀀓􀀅􀀐 Ac􀀅􀀗􀀑􀀄􀀍 􀀃f 􀀊􀀑􀀗􀀔c􀀔􀀓􀀑􀀎 L􀀅b􀀃􀀌􀀅􀀖􀀃􀀌􀀍 􀀃f Ex􀀝􀀑􀀌􀀔􀀄􀀑􀀓􀀖􀀅􀀐 􀀁􀀂􀀌􀀃􀀄b􀀃􀀆􀀔􀀆 (I􀀊EX-AN􀀊)􀀎 A􀀌􀀙􀀑􀀓􀀖􀀔􀀓􀀅  \n􀀈􀀂􀀑􀀓􀀙􀀐􀀅􀀓 􀀈􀀂􀀅􀀓􀀙􀀎  \nG􀀑􀀓􀀑􀀌􀀅􀀐 􀀜􀀃􀀆􀀝􀀔􀀖􀀅􀀐 􀀃f 􀀞􀀑􀀓􀀖􀀌􀀅􀀐 􀀁􀀂􀀑􀀅􀀖􀀑􀀌􀀞􀀃􀀄􀀄􀀅􀀓􀀗􀀎 􀀞􀀂􀀔􀀓􀀅  \n*􀀋􀀌􀀈􀀈􀀁􀀍􀀎􀀌􀀏􀀂􀀁􀀏􀀋􀀁  \nX􀀔􀀓 􀀈􀀋􀀓  \n 􀀗􀀃c􀀖􀀃􀀌􀀆􀀋􀀓x􀀔􀀓@􀀂􀀃􀀖􀀄􀀅􀀔􀀐􀀉c􀀃􀀄 W􀀑􀀓c􀀂􀀑􀀓􀀙 􀀊􀀅  \n 􀀙z􀀗􀀌􀀄􀀅@163􀀉c􀀃􀀄 P􀀑􀀔q􀀔􀀓􀀙 L􀀔  \n 􀀅􀀓􀀓􀀔􀀑 _ 129@126􀀉c􀀃􀀄  \n†􀀁􀀂􀀑􀀆􀀑 􀀅􀀋􀀖􀀂􀀃􀀌􀀆 􀀂􀀅􀀕􀀑 c􀀃􀀓􀀖􀀌􀀔b􀀋􀀖􀀑􀀗 􀀑q􀀋􀀅􀀐􀀐􀀍 􀀖􀀃􀀖􀀂􀀔􀀆 􀀛􀀃􀀌k 􀀅􀀓􀀗 􀀆􀀂􀀅􀀌􀀑 fi􀀌􀀆􀀖 􀀅􀀋􀀖􀀂􀀃􀀌􀀆􀀂􀀔􀀝  \n􀀈􀀁􀀋􀀁􀀃􀀉􀀁􀀂 11 A􀀝􀀌􀀔􀀐 2025  \n􀀐􀀋􀀋􀀁􀀎􀀄􀀁􀀂 08 􀀈􀀑􀀝􀀖􀀑􀀄b􀀑􀀌 2025  \n􀀎􀀑􀀆􀀒􀀃􀀍􀀓􀀁􀀂 10 Oc􀀖􀀃b􀀑􀀌 2025  \n􀀋􀀃􀀄􀀐􀀄􀀃􀀌􀀏  \n􀀈􀀂􀀔 X􀀎 W􀀅􀀓􀀙 X􀀎 􀀏􀀅􀀓􀀙 􀀜􀀎 F􀀅􀀓 X􀀎 L􀀔􀀋 G􀀎 􀀈􀀃􀀓􀀙 􀀏􀀎 P􀀑􀀓􀀙 Q􀀎 W􀀅􀀓􀀙 Q􀀎 􀀈􀀋􀀓 X􀀎 􀀊􀀅 W 􀀅􀀓􀀗 L􀀔 P (2025) Acc􀀋􀀌􀀅􀀖􀀑 􀀝􀀌􀀑􀀗􀀔c􀀖􀀔􀀃􀀓 􀀃f 􀀆􀀑􀀝􀀆􀀔􀀆 f􀀌􀀃􀀄 􀀝􀀑􀀗􀀔􀀅􀀖􀀌􀀔c 􀀑􀀄􀀑􀀌􀀙􀀑􀀓c􀀍 􀀗􀀑􀀝􀀅􀀌􀀖􀀄􀀑􀀓􀀖 􀀖􀀃 PI􀀞􀀒 􀀋􀀆􀀔􀀓􀀙 􀀅􀀄􀀅c􀀂􀀔􀀓􀀑-􀀐􀀑􀀅􀀌􀀓􀀔􀀓􀀙 􀀄􀀃􀀗􀀑􀀐􀀉  \nF􀀌􀀃􀀓􀀖􀀉 P􀀑􀀗􀀔􀀅􀀖􀀌􀀉 13:1610187􀀉  \n􀀗􀀃􀀔: 10􀀉3389/f􀀝􀀑􀀗􀀉2025􀀉1610187  \n􀀋􀀌􀀎􀀇􀀈􀀃􀀔􀀓􀀄  \n© 2025 􀀈􀀂􀀔􀀎 W􀀅􀀓􀀙􀀎 􀀏􀀅􀀓􀀙􀀎 F􀀅􀀓􀀎 L􀀔􀀋􀀎 􀀈􀀃􀀓􀀙􀀎 P􀀑􀀓􀀙􀀎 W􀀅􀀓􀀙􀀎 􀀈􀀋􀀓􀀎 􀀊􀀅 􀀅􀀓􀀗 L􀀔􀀉 􀀁􀀂􀀔􀀆 􀀔􀀆 􀀅􀀓 􀀃􀀝􀀑􀀓-􀀅cc􀀑􀀆􀀆􀀅􀀌􀀖􀀔c􀀐􀀑 􀀗􀀔􀀆􀀖􀀌􀀔b􀀋􀀖􀀑􀀗 􀀋􀀓􀀗􀀑􀀌 􀀖􀀂􀀑 􀀖􀀑􀀌􀀄􀀆 􀀃f 􀀖􀀂􀀑  \n􀀞􀀌􀀑􀀅􀀖􀀔􀀕􀀑 􀀞􀀃􀀄􀀄􀀃􀀓􀀆 A􀀖􀀖􀀌􀀔b􀀋􀀖􀀔􀀃􀀓 L􀀔c􀀑􀀓􀀆􀀑 (􀀞􀀞 B􀀏)􀀉 􀀁􀀂􀀑 􀀋􀀆􀀑􀀎 􀀗􀀔􀀆􀀖􀀌􀀔b􀀋􀀖􀀔􀀃􀀓 􀀃􀀌 􀀌􀀑􀀝􀀌􀀃􀀗􀀋c􀀖􀀔􀀃􀀓 􀀔􀀓􀀃􀀖􀀂􀀑􀀌 f􀀃􀀌􀀋􀀄􀀆 􀀔􀀆 􀀝􀀑􀀌􀀄􀀔􀀖􀀖􀀑􀀗􀀎 􀀝􀀌􀀃􀀕􀀔􀀗􀀑􀀗 􀀖􀀂􀀑􀀃􀀌􀀔􀀙􀀔􀀓􀀅􀀐 􀀅􀀋􀀖􀀂􀀃􀀌(􀀆) 􀀅􀀓􀀗 􀀖􀀂􀀑 c􀀃􀀝􀀍􀀌􀀔􀀙􀀂􀀖 􀀃􀀛􀀓􀀑􀀌(􀀆)􀀅􀀌􀀑 c􀀌􀀑􀀗􀀔􀀖􀀑􀀗 􀀅􀀓􀀗 􀀖􀀂􀀅􀀖 􀀖􀀂􀀑 􀀃􀀌􀀔􀀙􀀔􀀓􀀅􀀐 􀀝􀀋b􀀐􀀔c􀀅􀀖􀀔􀀃􀀓􀀔􀀓 􀀖􀀂􀀔􀀆 j􀀃􀀋􀀌􀀓􀀅􀀐 􀀔􀀆 c􀀔􀀖􀀑􀀗􀀎 􀀔􀀓 􀀅cc􀀃􀀌􀀗􀀅􀀓c􀀑 􀀛􀀔􀀖􀀂􀀅cc􀀑􀀝􀀖􀀑􀀗 􀀅c􀀅􀀗􀀑􀀄􀀔c 􀀝􀀌􀀅c􀀖􀀔c􀀑􀀉 N􀀃 􀀋􀀆􀀑􀀎􀀗􀀔􀀆􀀖􀀌􀀔b􀀋􀀖􀀔􀀃􀀓 􀀃􀀌 􀀌􀀑􀀝􀀌􀀃􀀗􀀋c􀀖􀀔􀀃􀀓 􀀔􀀆 􀀝􀀑􀀌􀀄􀀔􀀖􀀖􀀑􀀗􀀛􀀂􀀔c􀀂 􀀗􀀃􀀑􀀆 􀀓􀀃􀀖 c􀀃􀀄􀀝􀀐􀀍 􀀛􀀔􀀖􀀂 􀀖􀀂􀀑􀀆􀀑 􀀖􀀑􀀌􀀄􀀆􀀉  \n􀀁􀀂􀀂􀀃􀀄􀀅􀀆􀀇 􀀉􀀄􀀇􀀊􀀋􀀂􀀆􀀋􀀌􀀍 􀀌􀀎 􀀏􀀇􀀉􀀏􀀋􀀏􀀎􀀄􀀌􀀐 􀀉􀀇􀀊􀀋􀀅􀀆􀀄􀀋􀀂 􀀇􀀐􀀇􀀄􀀑􀀇􀀍􀀂􀀒􀀊􀀇􀀉􀀅􀀄􀀆􀀐􀀇􀀍􀀆 􀀆􀀌 􀀓􀀔􀀕􀀖 􀀃􀀏􀀋􀀍􀀑 􀀅􀀐􀀅􀀂􀀗􀀋􀀍􀀇􀀘􀀙􀀇􀀅􀀄􀀍􀀋􀀍􀀑 􀀐􀀌􀀊􀀇􀀙  \n􀀚􀀃􀀅􀀍 􀀛􀀗􀀋 􀀜􀀝􀀞 􀀚􀀃􀀒􀀋􀀍􀀑 􀀅􀀍􀀑 2􀀝􀀞 H􀀅􀀌􀀐􀀇􀀋 Y􀀅􀀍􀀑 􀀜􀀝􀀞 􀀚􀀋􀀅􀀌w􀀇􀀋 F􀀅􀀍 􀀜􀀞 G􀀃􀀅􀀍􀀑􀀐􀀋􀀍􀀑 L􀀋􀀃 􀀜􀀞 Y􀀌􀀍􀀑􀀙􀀋􀀍􀀑 􀀛􀀌􀀍􀀑 􀀜􀀞 Q􀀋􀀃􀀒􀀅􀀍 􀀓􀀇􀀍􀀑 􀀜􀀞 Q􀀋􀀅􀀍􀀑 􀀅􀀍􀀑 􀀜􀀞􀀚􀀋􀀍 􀀛􀀃􀀍 3*􀀞 􀀇􀀍􀀂􀀗􀀇􀀍􀀑 M􀀅 􀀜* 􀀅􀀍􀀊 􀀓􀀇􀀋q􀀋􀀍􀀑 L􀀋 􀀜*  \n1P􀀑􀀗􀀔􀀅􀀖􀀌􀀔c E􀀄􀀑􀀌􀀙􀀑􀀓c􀀍 D􀀑􀀝􀀅􀀌􀀖􀀄􀀑􀀓􀀖􀀎 G􀀋􀀅􀀓􀀙z􀀂􀀃􀀋 W􀀃􀀄􀀑􀀓 􀀅􀀓􀀗 􀀞􀀂􀀔􀀐􀀗􀀌􀀑􀀓’􀀆 􀀊􀀑􀀗􀀔c􀀅􀀐 􀀞􀀑􀀓􀀖􀀑􀀌􀀎 G􀀋􀀅􀀓􀀙z􀀂􀀃􀀋􀀊􀀑􀀗􀀔c􀀅􀀐 􀀒􀀓􀀔􀀕􀀑􀀌􀀆􀀔􀀖􀀍􀀎 G􀀋􀀅􀀓􀀙􀀗􀀃􀀓􀀙 P􀀌􀀃􀀕􀀔􀀓c􀀔􀀅􀀐 􀀞􀀐􀀔􀀓􀀔c􀀅􀀐 􀀚􀀑􀀆􀀑􀀅􀀌c􀀂 􀀞􀀑􀀓􀀖􀀑􀀌 f􀀃􀀌 􀀞􀀂􀀔􀀐􀀗 􀀜􀀑􀀅􀀐􀀖􀀂􀀎 G􀀋􀀅􀀓􀀙z􀀂􀀃􀀋􀀎􀀞􀀂􀀔􀀓􀀅􀀎 2E􀀛􀀑􀀐􀀐 􀀁􀀑c􀀂􀀓􀀃􀀐􀀃􀀙􀀍 􀀞􀀃􀀄􀀝􀀅􀀓􀀍􀀎 􀀜􀀅􀀓􀀙z􀀂􀀃􀀋􀀎 􀀞􀀂􀀔􀀓􀀅􀀎 3􀀊􀀑􀀗􀀔c􀀅􀀐 D􀀑􀀝􀀅􀀌􀀖􀀄􀀑􀀓􀀖 􀀃f G􀀋􀀅􀀓􀀙z􀀂􀀃􀀋 W􀀃􀀄􀀑􀀓􀀅􀀓􀀗 􀀞􀀂􀀔􀀐􀀗􀀌􀀑􀀓’􀀆 􀀊􀀑􀀗􀀔c􀀅􀀐 􀀞􀀑􀀓􀀖􀀑􀀌􀀎 G􀀋􀀅􀀓􀀙z􀀂􀀃􀀋 􀀊􀀑􀀗􀀔c􀀅􀀐 􀀒􀀓􀀔􀀕􀀑􀀌􀀆􀀔􀀖􀀍􀀎 G􀀋􀀅􀀓􀀙􀀗􀀃􀀓􀀙 P􀀌􀀃􀀕􀀔􀀓c􀀔􀀅􀀐 􀀞􀀐􀀔􀀓􀀔c􀀅􀀐􀀚􀀑􀀆􀀑􀀅􀀌c􀀂 􀀞􀀑􀀓􀀖􀀑􀀌 f􀀃􀀌 􀀞􀀂􀀔􀀐􀀗 􀀜􀀑􀀅􀀐􀀖􀀂􀀎 G􀀋􀀅􀀓􀀙z􀀂􀀃􀀋􀀎 􀀞􀀂􀀔􀀓􀀅  \n􀀆􀀕􀀖􀀗􀀘􀀙􀀚􀀛􀀜􀀝􀀞 􀀁􀀔􀀄􀀑􀀐􀀍 􀀔􀀗􀀑􀀓􀀖􀀔fic􀀅􀀖􀀔􀀃􀀓 􀀃f 􀀝􀀑􀀗􀀔􀀅􀀖􀀌􀀔c 􀀆􀀑􀀝􀀆􀀔􀀆 􀀌􀀑􀀄􀀅􀀔􀀓􀀆 􀀅 c􀀌􀀔􀀖􀀔c􀀅􀀐 c􀀂􀀅􀀐􀀐􀀑􀀓􀀙􀀑 􀀔􀀓 􀀑􀀄􀀑􀀌􀀙􀀑􀀓c􀀍 􀀅􀀓􀀗 􀀔􀀓􀀖􀀑􀀓􀀆􀀔􀀕􀀑 c􀀅􀀌􀀑 􀀆􀀑􀀖􀀖􀀔􀀓􀀙􀀆 􀀗􀀋􀀑 􀀖􀀃 􀀖􀀂􀀑 􀀂􀀑􀀖􀀑􀀌􀀃􀀙􀀑􀀓􀀑􀀃􀀋􀀆 c􀀐􀀔􀀓􀀔c􀀅􀀐 􀀝􀀌􀀑􀀆􀀑􀀓􀀖􀀅􀀖􀀔􀀃􀀓􀀆 􀀅c􀀌􀀃􀀆􀀆 􀀅􀀙􀀑 􀀙􀀌􀀃􀀋􀀝􀀆􀀉 Ex􀀔􀀆􀀖􀀔􀀓􀀙 􀀆c􀀃􀀌􀀔􀀓􀀙 􀀆􀀍􀀆􀀖􀀑􀀄􀀆 􀀃f􀀖􀀑􀀓 􀀐􀀅ck 􀀖􀀑􀀄􀀝􀀃􀀌􀀅􀀐 􀀌􀀑􀀆􀀃􀀐􀀋􀀖􀀔􀀃􀀓 􀀅􀀓􀀗 􀀔􀀓􀀖􀀑􀀌􀀝􀀌􀀑􀀖􀀅b􀀔􀀐􀀔􀀖􀀍􀀉 W􀀑 􀀅􀀔􀀄􀀑􀀗 􀀖􀀃 􀀗􀀑􀀕􀀑􀀐􀀃􀀝 􀀅 􀀌􀀑􀀅􀀐-􀀖􀀔􀀄􀀑􀀎􀀄􀀅c􀀂􀀔􀀓􀀑 􀀐􀀑􀀅􀀌􀀓􀀔􀀓􀀙–b􀀅􀀆􀀑􀀗 􀀝􀀌􀀑􀀗􀀔c􀀖􀀔􀀃􀀓 f􀀌􀀅􀀄􀀑􀀛􀀃􀀌k 􀀔􀀓􀀖􀀑􀀙􀀌􀀅􀀖􀀔􀀓􀀙 􀀆􀀖􀀅􀀖􀀔c 􀀅􀀓􀀗 􀀗􀀍􀀓􀀅􀀄􀀔c 􀀑􀀐􀀑c􀀖􀀌􀀃􀀓􀀔c 􀀂􀀑􀀅􀀐􀀖􀀂 􀀌􀀑c􀀃􀀌􀀗 (E􀀜􀀚) f􀀑􀀅􀀖􀀋􀀌􀀑􀀆 􀀖􀀃 􀀆􀀋􀀝􀀝􀀃􀀌􀀖 􀀑􀀅􀀌􀀐􀀍 􀀆􀀑􀀝􀀆􀀔􀀆 􀀗􀀑􀀖􀀑c􀀖􀀔􀀃􀀓􀀉  \neth􀀚􀀝s􀀞 􀀁􀀂􀀔􀀆 􀀌􀀑􀀖􀀌􀀃􀀆􀀝􀀑c􀀖􀀔􀀕􀀑 􀀆􀀖􀀋􀀗􀀍 􀀔􀀓c􀀐􀀋􀀗􀀑􀀗 􀀝􀀑􀀗􀀔􀀅􀀖􀀌􀀔c 􀀝􀀅􀀖􀀔􀀑􀀓􀀖􀀆 f􀀌􀀃􀀄 G􀀋􀀅􀀓􀀙z􀀂􀀃􀀋 W􀀃􀀄􀀑􀀓 􀀅􀀓􀀗 􀀞􀀂􀀔􀀐􀀗􀀌􀀑􀀓’􀀆 􀀊􀀑􀀗􀀔c􀀅􀀐 􀀞􀀑􀀓􀀖􀀑􀀌 (GW􀀞􀀊􀀞; 􀀁 = 1􀀎697) 􀀅􀀓􀀗 􀀅􀀓 􀀑x􀀖􀀑􀀌􀀓􀀅􀀐􀀕􀀅􀀐􀀔􀀗􀀅􀀖􀀔􀀃􀀓 c􀀃􀀂􀀃􀀌􀀖 f􀀌􀀃􀀄 􀀖􀀂􀀑 􀀊I􀀊I􀀞-III 􀀗􀀅􀀖􀀅b􀀅􀀆􀀑 (􀀁 = 827)􀀉 I􀀌􀀌􀀑􀀙􀀋􀀐􀀅􀀌 􀀖􀀔􀀄􀀑-􀀆􀀑􀀌􀀔􀀑􀀆􀀗􀀅􀀖􀀅 􀀛􀀑􀀌􀀑 􀀔􀀄􀀝􀀋􀀖􀀑􀀗 􀀋􀀆􀀔􀀓􀀙 􀀅 c􀀃􀀌􀀌􀀑􀀐􀀅􀀖􀀔􀀃􀀓-􀀑􀀓􀀂􀀅􀀓c􀀑􀀗 c􀀃􀀓􀀖􀀔􀀓􀀋􀀃􀀋􀀆 􀀖􀀔􀀄􀀑-􀀛􀀔􀀓􀀗􀀃􀀛􀀂􀀔􀀆􀀖􀀃􀀙􀀌􀀅􀀄 􀀛􀀔􀀖􀀂 􀀄􀀋􀀐􀀖􀀔􀀕􀀅􀀌􀀔􀀅􀀖􀀑 G􀀅􀀋􀀆􀀆􀀔􀀅􀀓 􀀝􀀌􀀃c􀀑􀀆􀀆􀀑􀀆 (􀀞􀀁W􀀜 + 􀀊GP)􀀉 W􀀑 c􀀃􀀄􀀝􀀅􀀌􀀑􀀗 􀀖􀀂􀀑 􀀝􀀌􀀑􀀗􀀔c􀀖􀀔􀀕􀀑 􀀝􀀑􀀌f􀀃􀀌􀀄􀀅􀀓c􀀑 􀀃f XGB􀀃􀀃􀀆􀀖 􀀅􀀓􀀗 􀀙􀀅􀀖􀀑􀀗 􀀌􀀑c􀀋􀀌􀀌􀀑􀀓􀀖 􀀋􀀓􀀔􀀖(G􀀚􀀒)-b􀀅􀀆􀀑􀀗 􀀚NN 􀀄􀀃􀀗􀀑􀀐􀀆 􀀃􀀕􀀑􀀌 􀀅 12-􀀂 􀀛􀀔􀀓􀀗􀀃􀀛 􀀝􀀌􀀔􀀃􀀌 􀀖􀀃 c􀀐􀀔􀀓􀀔c􀀅􀀐 􀀗􀀔􀀅􀀙􀀓􀀃􀀆􀀔􀀆􀀉􀀊􀀃􀀗􀀑􀀐 􀀃􀀋􀀖􀀝􀀋􀀖􀀆 􀀛􀀑􀀌􀀑 􀀕􀀅􀀐􀀔􀀗􀀅􀀖􀀑􀀗 􀀔􀀓􀀖􀀑􀀌􀀓􀀅􀀐􀀐􀀍 􀀅􀀓􀀗 􀀑x􀀖􀀑􀀌􀀓􀀅􀀐􀀐􀀍 􀀋􀀆􀀔􀀓􀀙 A􀀒􀀚O􀀞􀀎 A􀀒P􀀚􀀞􀀎􀀅􀀓􀀗 􀀏􀀃􀀋􀀗􀀑􀀓 􀀔􀀓􀀗􀀑x􀀎 􀀛􀀔􀀖􀀂 􀀈􀀜AP-b􀀅􀀆􀀑􀀗 􀀔􀀓􀀖􀀑􀀌􀀝􀀌􀀑􀀖􀀅b􀀔􀀐􀀔􀀖􀀍 􀀅􀀝􀀝􀀐􀀔􀀑􀀗 􀀖􀀃 􀀔􀀗􀀑􀀓􀀖􀀔f􀀍 k􀀑􀀍 c􀀐􀀔􀀓􀀔c􀀅􀀐 f􀀑􀀅􀀖􀀋􀀌􀀑􀀆􀀉  \n􀀈es􀀛lts􀀞 􀀁􀀂􀀑 􀀞􀀁W􀀜 + 􀀊GP-XGB􀀃􀀃􀀆􀀖 􀀄􀀃􀀗􀀑􀀐 􀀅c􀀂􀀔􀀑􀀕􀀑􀀗 􀀖􀀂􀀑 􀀂􀀔􀀙􀀂􀀑􀀆􀀖 A􀀒􀀚O􀀞 􀀅􀀖􀀗􀀔􀀅􀀙􀀓􀀃􀀆􀀔􀀆 􀀖􀀔􀀄􀀑 (􀀁 = 0 􀀂; A􀀒􀀚O􀀞 = 0􀀉915)􀀎 􀀛􀀂􀀔􀀐􀀑 􀀖􀀂􀀑 G􀀚􀀒-b􀀅􀀆􀀑􀀗 􀀄􀀃􀀗􀀑􀀐􀀗􀀑􀀄􀀃􀀓􀀆􀀖􀀌􀀅􀀖􀀑􀀗 􀀆􀀋􀀝􀀑􀀌􀀔􀀃􀀌 􀀖􀀑􀀄􀀝􀀃􀀌􀀅􀀐 􀀆􀀖􀀅b􀀔􀀐􀀔􀀖􀀍 􀀅c􀀌􀀃􀀆􀀆 􀀑􀀅􀀌􀀐􀀍 􀀛􀀔􀀓􀀗􀀃􀀛􀀆􀀉 􀀁􀀃􀀝 c􀀃􀀓􀀖􀀌􀀔b􀀋􀀖􀀔􀀓􀀙 f􀀑􀀅􀀖􀀋􀀌􀀑􀀆 􀀔􀀓c􀀐􀀋􀀗􀀑􀀗 􀀐􀀅c􀀖􀀅􀀖􀀑􀀎 􀀛􀀂􀀔􀀖􀀑 b􀀐􀀃􀀃􀀗 c􀀑􀀐􀀐 c􀀃􀀋􀀓􀀖􀀎 􀀝􀀜􀀎 􀀅􀀓􀀗􀀕􀀅􀀆􀀃􀀝􀀌􀀑􀀆􀀆􀀃􀀌 􀀋􀀆􀀑􀀉 Ex􀀖􀀑􀀌􀀓􀀅􀀐 􀀕􀀅􀀐􀀔􀀗􀀅􀀖􀀔􀀃􀀓 c􀀃􀀓fi􀀌􀀄􀀑􀀗 􀀙􀀑􀀓􀀑􀀌􀀅􀀐􀀔z􀀅b􀀔􀀐􀀔􀀖􀀍 (􀀊I􀀊I􀀞-IIIA􀀒􀀚O􀀞 = 0􀀉905)􀀉 􀀈􀀔􀀄􀀋􀀐􀀅􀀖􀀔􀀃􀀓 􀀃f 􀀌􀀑􀀅􀀐-􀀖􀀔􀀄􀀑 􀀅􀀐􀀑􀀌􀀖􀀆 􀀆􀀂􀀃􀀛􀀑􀀗 􀀅 􀀄􀀑􀀗􀀔􀀅􀀓 􀀐􀀑􀀅􀀗 􀀖􀀔􀀄􀀑 􀀃f 6􀀉2 􀀂 b􀀑f􀀃􀀌􀀑 c􀀐􀀔􀀓􀀔c􀀅􀀐 􀀗􀀔􀀅􀀙􀀓􀀃􀀆􀀔􀀆􀀎 􀀛􀀔􀀖􀀂 κ = 0􀀉82 􀀅􀀙􀀌􀀑􀀑􀀄􀀑􀀓􀀖 􀀅􀀙􀀅􀀔􀀓􀀆􀀖 􀀝􀀂􀀍􀀆􀀔c􀀔􀀅􀀓 -c􀀃􀀓fi􀀌􀀄􀀑􀀗 c􀀅􀀆􀀑􀀆􀀉  \n􀀋􀀚􀀜􀀖l􀀛si􀀚􀀜s􀀞 O􀀋􀀌 􀀌􀀑􀀆􀀋􀀐􀀖􀀆 􀀆􀀋􀀙􀀙􀀑􀀆􀀖 􀀖􀀂􀀅􀀖 􀀅 􀀗􀀋􀀅􀀐-􀀄􀀃􀀗􀀑􀀐 􀀑􀀓􀀆􀀑􀀄b􀀐􀀑 c􀀃􀀄b􀀔􀀓􀀔􀀓􀀙􀀔􀀓􀀖􀀑􀀌􀀝􀀃􀀐􀀅􀀖􀀔􀀃􀀓-b􀀅􀀆􀀑􀀗 􀀝􀀌􀀑􀀝􀀌􀀃c􀀑􀀆􀀆􀀔􀀓􀀙 􀀅􀀓􀀗 􀀔􀀓􀀖􀀑􀀌􀀝􀀌􀀑􀀖􀀅b􀀐􀀑 􀀄􀀅c􀀂􀀔􀀓􀀑 􀀐􀀑􀀅􀀌􀀓􀀔􀀓􀀙􀀑􀀓􀀅b􀀐􀀑􀀆 􀀌􀀃b􀀋􀀆􀀖 􀀑􀀅􀀌􀀐􀀍 􀀆􀀑􀀝􀀆􀀔","cbCaiqFx1d0kllCz","https://ap.wps.com/l/cbCaiqFx1d0kllCz","pdf",1754503,8,1,15,"English","en",105,"# Study overview\n## Data sources and cohorts\n## Machine-learning models and features\n## Evaluation metrics and results","[{\"question\":\"What is the document’s main goal?\",\"answer\":\"To develop and assess a machine-learning model that predicts sepsis progression from the pediatric emergency department to PICU.\"},{\"question\":\"What data source is used for the sepsis prediction model?\",\"answer\":\"The model is built using electronic health record information available in the clinical setting.\"},{\"question\":\"How is the model’s performance evaluated?\",\"answer\":\"Performance is assessed using discrimination and related evaluation metrics, with analysis of calibration and reported results across modeling approaches.\"}]","Accurate prediction of sepsis from pediatric emergency department to PICU using a machine-learning model | 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