A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage.
Shilpa J PatelDaniel B ChamberlainJames M ChamberlainPublished in: Academic emergency medicine : official journal of the Society for Academic Emergency Medicine (2018)
Three of the four machine learning models performed well with decision trees preforming the worst. The gradient boosting machines model demonstrated a slight advantage over other approaches at predicting need for hospital-level care at the time of triage in pediatric patients presenting with asthma exacerbation. The addition of weight, SES, and weather data improved the performance of this model.
Keyphrases
- emergency department
- chronic obstructive pulmonary disease
- machine learning
- lung function
- big data
- healthcare
- artificial intelligence
- palliative care
- body mass index
- adverse drug
- allergic rhinitis
- physical activity
- deep learning
- weight gain
- young adults
- decision making
- intensive care unit
- body weight
- acute respiratory distress syndrome
- health insurance
- data analysis