| abstract |
Technologies are provided for leveraging machine learning to predict the likelihood of near-future OUD for patients presenting in an emergency department. A model is trained with data corresponding to one or more of: gender, age, prior opioid use disorder diagnosis, prior opioid use disorder events, prior opioids, prior emergency department encounters, prior inpatient encounters, other medications, drug screening tests, hepatitis C tests, tobacco use questionnaires, prior results from medical tests, social history questionnaires, or other diagnoses to predict opioid use disorder risk for a population of patients. Upon receiving information available at an emergency department triage for a patient, the trained model is utilized to predict the opioid use disorder risk for the patient over a predetermined period of time. The predicted opioid use disorder risk for the patient is provided within the patient chart over the predetermined period of time and may include details corresponding to risk factors specific to the patient and risk mitigation options. |