Predicting emergency department orders with multilabel machine learning techniques and simulating effects on length of stay.

ABSTRACT:

Objective

Emergency departments (EDs) continue to pursue optimal patient flow without sacrificing quality of care. The speed with which a healthcare provider receives pertinent information, such as results from clinical orders, can impact flow. We seek to determine if clinical ordering behavior can be predicted at triage during an ED visit.

Materials and methods

Using data available during triage, we trained multilabel machine learning classifiers to predict clinical orders placed during an ED visit. We benchmarked 4 classifiers with 2 multilabel learning frameworks that predict orders independently (binary relevance) or simultaneously (random k-labelsets). We evaluated algorithm performance, calculated variable importance, and conducted a simple simulation study to examine

SUBMITTER: Hunter-Zinck HS 

PROVIDER: S-EPMC7647143 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature