Year: 2019
Type: conference-paper
Study design: Observational Study
Abstract: Diabetes poses a variety of medical complications to patients, resulting in a high rate of unplanned medical visits, which are costly to patients and healthcare providers alike. However, unplanned medical visits by their nature are very difficult to predict. The current project draws upon electronic health records (EMR’s) of adult patients with diabetes who received care at Sanford Health between 2014 and 2017. Various machine learning methods were used to predict which patients have had an unplanned medical visit based on a variety of EMR variables (age, BMI, blood pressure, # of prescriptions, # of diagnoses on problem list, A1C, HDL, LDL, and a ranked variable for tobacco use severity). A radial-basis support vector machine (SVM) was the most accurate method, achieving a hit rate of 68.5% and a correct rejection rate of 62.9% during cross-validation testing. Follow-up testing of the trained SVM indicated that, of the modifiable prediction variables, high blood pressure and low levels of high-density lipoprotein (HDL) were most strongly predictive of unplanned medical visits. Future directions include refining and validating the predictive model, towards the ultimate goal of developing and implementing clinical recommendations for preventing unplanned medical visits among adult patients with diabetes.
Authors: Arielle Selya, Eric L. Johnson
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2912327596