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Predicting progression to septic shock in the emergency department using an externally generalizable machine learning algorithm

OBJECTIVE: Machine-learning (ML) algorithms allow for improved prediction of sepsis syndromes in the ED using data from electronic medical records. Transfer learning, a new subfield of ML, allows for generalizability of an algorithm across clinical sites. We aimed to validate the Artificial Intellig...

詳細記述

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書誌詳細
出版年:medRxiv
主要な著者: Wardi, Gabriel, Carlile, Morgan, Holder, Andre, Shashikumar, Supreeth, Hayden, Stephen R, Nemati, Shamim
フォーマット: Artigo
言語:Inglês
出版事項: Cold Spring Harbor Laboratory 2020
主題:
オンライン・アクセス:https://ncbi.nlm.nih.gov/pmc/articles/PMC7654881/
https://ncbi.nlm.nih.gov/pubmed/33173889
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1101/2020.11.02.20224931
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