Patient Reported Symptom Dental Infection Extreme Learning Machine
Posted Date: Jun 17, 2025
- Investigator: Eric Holmes
- Specialties:
- Type of Study: Observational/Survey
BACKGROUND: Every year millions of people in the United States seek dental care in emergency departments (ED). The annual cost of these visits has risen over the past decades and now accounts for over a billion dollars of healthcare spending. ED treatment often consists of antibiotic therapy supplemented with analgesic medication without any definitive dental treatment. Extreme Learning Machines (ELMs) are a type of artificial neural network which has shown tremendous ability to create classification models to solve various problems in medicine. This study aims to assess if an ELM could accurately predict the necessary treatment regimen for a patient presenting to the ED for a non-traumatic dental condition (NTDC). METHODS: This retrospective cohort will utilize electronic health records (EHR) data from UC Health EDs in Cincinnati, Ohio. To be included subjects must have presented to a UC Health ED between October 2nd, 2015, and October 2nd, 2025, with a primary diagnosis code of K02.9, K04.4, K04.7, or K08.9, and must have been at least 18 years of age at the time of their visit. The primary predictor variables in the ELM model are several demographic factors and presenting symptoms. The primary outcome variable is the discharge plan for the patient, classified as either simple or complicated management. ELMs will be created using several activations functions. All activation functions were tested with 50, 75, 100, 125, 150, and 175 neurons to test their accuracy.
Criteria:
To Be Included Patients Must Have Presented To A Uc Er Between 10/2/2015 And 10/2/2025 With A Clinical Impression Or Diagnosis Indicative Of A Dental Infection And Have Been Over 18 At That Time.
Keywords:
Electronic Learning Machine, Dental Infection, Ai
For More Information:
Eric Holmes
513-584-2094
eric.holmes@uc.edu