Dental Infection Extreme Learning Machine
Posted Date: Apr 23, 2024
- Investigator: Eric Holmes
- Specialties:
- Type of Study: Observational/Survey
The purpose of this study is to see if an extreme learning machine (ELM) can differentiate between the treatment regimens necessary for a patient presenting with a dental infection based upon their presenting symptomology as well some of their demographic characteristics, and if this is possible how precisely a model can be. The objective of this study is to train an ELM on a dataset of patients presenting to UC Health emergency departments with a dental infection between October 2nd 2015 and January 2nd 2024. The dataset will consist of the patient’s presenting symptoms as well as their demographic characteristics. The specific aim of the ELM model will be to predict which patients require only a prescription for oral antibiotics and which patients require further treatment including but not limited to Intravenous (IV) antibiotics or fluids, or admission to the hospital.
Criteria:
Patients Must Be Over 18, Not Pregnant, Nor From A Protected Population Listed In Section 10.3 Of Hrp-503. Patient Must Have Visited A Uc Er Between 10-2-2015 And 1-2-2024 With A Clinical Impression Which Was Indicative Of A Dental Infection.
Keywords:
Odontogenic, Extreme Learning Machine, Modeling
For More Information:
Eric Holmes
541-405-6335
eric.holmes@uc.edu