Machine Learning Approaches to Predict Patient’s Length of Stay in Emergency Department

Table 1

Input data set attributes, types, and definitions.

Category	Attribute	Definition
Check-in data	Date	Day, month, and year of arrival
	Day	The name of the day (Sunday, Monday … etc.)
	ID	Identity document of the patient in the hospital
	Gender	Male\female
	Insurance	Insurance info
	Mode of arrival	Patient’s arrival mode
	Age	Age of the patient
Medical procedure	Immediate treatment	Immediate treatment requirements
	Triage level	Urgency case level (1–5)
	Medication	Medication needed (yes, no)
	Consultation	Consultation needed (yes, no)
Time	T arrive	The arrival time
	T triage assessment	Triage assessment time
	T NURS assessment	Nurse assessment time
	T doctor assessment	Doctor assessment time
	T departure	Patient’s departure time
Medical tests	Twenty-three tests, including urine analysis, CBC, cardiac enzymes, stool analysis, X-ray, ultrasound, CT scan, and MRI	Tests
Others	Number of nurses	Available number of nurses
	Crowding	Number of patients in the ED
	Lockdown	Lockdown status
	LOS	(T departure-T arrival)