Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study
Original Paper
[키워드] 48 hour
acute respiratory distress
Additive
Admission
age
albumin
all-cause mortality
ARDS
artificial intelligence
AUROC
Bacterial
cardiac troponin
characteristic
clinical care
Clinical data
clinical experience
Cohort
Combination
COVID-19
COVID-19 cohort
data
Deterioration
develop
diagnosed
disease
Diseases
exploratory
feasibility
feasible
Features
framework
Hispanic ethnicity
Hospitalized
identify
IMV
Infection
Influenza
information
Invasive mechanical ventilation
Laboratory
lack
learning
limitation
machine learning
machine learning model
Model
Mortality
Negative predictive value
objective
outcome
oxygen saturation
pandemic
Patient
patients
patients with comorbidity
patients with COVID-19
Pneumonia
Positive predictive value
positive predictive values
predict
predictor
Prognosis
proportion
provided
randomized trials
Research
respiratory
respiratory diseases
restrict
Result
retrospective cohort
sensitivity
significantly
smoking
specificity
syndrome
the patient
training data
Treatment
Viral pneumonia
vital sign
White blood cell
with COVID-19
[DOI] 10.2196/23026 PMC 바로가기 [Article Type] Original Paper
[DOI] 10.2196/23026 PMC 바로가기 [Article Type] Original Paper