Machine Learning Applied to Electronic Health Records: Identification of Chemotherapy Patients at High Risk for Preventable Emergency Department Visits and Hospital Admissions.
Dylan J PetersonNicolai P OstbergDouglas W BlayneyJames D BrooksTina Hernandez BoussardPublished in: JCO clinical cancer informatics (2022)
Dense EHR data can identify patients at risk for ACU using ML with promising accuracy. These models have potential to improve cancer care outcomes, patient experience, and costs by allowing for targeted, preventative interventions.
Keyphrases
- electronic health record
- emergency department
- machine learning
- end stage renal disease
- ejection fraction
- newly diagnosed
- prognostic factors
- type diabetes
- physical activity
- radiation therapy
- adverse drug
- metabolic syndrome
- deep learning
- patient reported
- patient reported outcomes
- cancer therapy
- weight loss
- climate change