Predicting the Need For Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model.

Journal: Shock (Augusta, Ga.)

PMID: 33177372

Abstract

BACKGROUND: Previous models on prediction of shock mostly focused on septic shock and often required laboratory results in their models. The purpose of this study was to use deep learning approaches to predict vasopressor requirement for critically ill patients within 24 h of intensive care unit (ICU) admission using only vital signs.

Authors

Gloria Hyunjung Kwak

Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Pokfulam, Hong Kong.
Lowell Ling

Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong, China. lowell.ling@cuhk.edu.hk.
Pan Hui

HKUST-DT System and Media Laboratory, Hong Kong University of Science and Technology, HongKong.

Keywords

Aged Cohort Studies Critical Illness Deep Learning Female Humans Intensive Care Units Male Middle Aged Models, Theoretical Needs Assessment Retrospective Studies Vasoconstrictor Agents Vital Signs

External Resources

View on PubMed Access via DOI PubMed (33177372)

Predicting the Need For Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model.

Abstract

Authors

Keywords

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