Beste Altınay on MFDM: MRI Free Decision Model for Diagnosis and Treatment Selection in Patients with Low Back and Neck Pain

Beste Altınay will give a seminar on “MFDM: MRI Free Decision Model for Diagnosis and Treatment Selection in Patients with Low Back and Neck Pain” on 31 May at 12:45. The abstract of the talk and a short bio is shared below.

Beste Mimaroğlu Altınay is currently a PhD Candidate at the Department of Medical Informatics, Middle East Technical University. She received her B.S. degree in Computer Engineering from Başkent University. She worked as a software specialist in the field of Hospital Information Management Systems. Afterwards, she worked as a research assistant in the Department of Management Information Systems at Cumhuriyet University and has been working as an IT Software Manager at Social Sciences University of Ankara since 2013.

Low back pain (LBP) and neck pain (NP) is a worldwide public health problem that affects life quality. Our goal was to develop a machine learning model that can direct LBP and NP patients for the appropriate treatment without magnetic resonance imaging (MRI) findings, thus reducing the demand for MRI and its burden on the health system. To evaluate the treatment outcomes, demographic information, clinical findings, and preoperative evaluation of pain, movement restriction, and pain data duration are analyzed from patient data in the pain clinic. Support Vector Machine (SVM) models are built by analyzing ten different attributes from 1482 patient data to classify correct treatment: drug, RF/IDET, or surgical intervention. The stepwise model proposed here classifies drug therapy patients with an 84% success ratio and can direct patients to surgery or RF/IDET with a 74.47% success ratio without MRI results. The proposed MRI Free Decision Model (MFDM) can be utilized in primary healthcare facilities to direct the patients to the appropriate treatment options without MRI, reducing the cost and load on the healthcare system while benefiting the patient by reducing the time to initiate the treatment.