US 12,148,529 B2
E-triage: an electronic emergency triage system
Scott R. Levin, Baltimore, MD (US); Thomas Kirsch, Baltimore, MD (US); Matthew Toerper, Cockeysville, MD (US); Gabor D. Kelen, Baltimore, MD (US); and Andrea Dugas, Silver Spring, MD (US)
Assigned to THE JOHNS HOPKINS UNIVERSITY, Baltimore, MD (US)
Filed by THE JOHNS HOPKINS UNIVERSITY, Baltimore, MD (US)
Filed on Aug. 19, 2019, as Appl. No. 16/544,016.
Application 16/544,016 is a continuation of application No. 15/435,746, filed on Feb. 17, 2017, abandoned.
Claims priority of provisional application 62/296,753, filed on Feb. 18, 2016.
Prior Publication US 2020/0005188 A1, Jan. 2, 2020
Int. Cl. G16H 50/20 (2018.01); G06N 5/01 (2023.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G16H 10/60 (2018.01); G16H 50/30 (2018.01)
CPC G16H 50/20 (2018.01) [G06N 5/01 (2023.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G16H 10/60 (2018.01); G16H 50/30 (2018.01)] 24 Claims
OG exemplary drawing
 
1. A method for performing a triage, the method comprising:
receiving, by a computing device having a trained machine learning prediction engine, data pertaining to an individual visiting an emergency department, the data pertaining to the individual including health-related information of the individual, wherein:
the trained machine learning prediction engine having been trained using supervised machine learning and training data related to a plurality of emergency department visits by individuals of an emergency department population, and
the trained machine learning prediction engine having used ensemble learning to create separate decision forest models, each of which being for prediction of a respective outcome of a plurality of predefined future acute clinical outcomes for the individuals of the emergency department population;
applying, in tandem by the computing device, the data pertaining to the individual to the separate decision forest models for prediction of the respective outcomes of the plurality of future acute clinical outcomes for the individual of the emergency department population to produce predicted probabilities of a risk for each of the respective outcomes for the individual of the emergency department population;
mapping, by the computing device, one of the produced predicted probabilities of the risk for the respective outcomes to one of a plurality of triage levels, the one of the plurality of triage levels being required to direct care for the individual in accordance with the one of the plurality of triage levels, the one of the produced predicted probabilities being mapped is based on values of the produced predicted probabilities;
making available, by the computing device, information regarding the one of the plurality of triage levels;
providing, by the computing device, an override feature to allow a health care provider to override the one of the plurality of triage levels;
collecting, by the computing device, a frequency of an override, a type of the override, and optionally reasons for the override; and
analyzing, by the computing device, the collected frequency of the override, the type of the override, and optionally the reasons for the override to iteratively improve an accuracy of the mapping of the one of the produced predicted probabilities of the risk for the respective outcomes to the one of the plurality of triage levels.