Ahmet Demirkaya
demirkaya@ece.neu.edu
ahmet-demirkaya
0000-0002-5859-6375
ahmetdemirkayaee
https://www.linkedin.com/in/ahmet-demirkaya

College of Engineering
EXP 710
Boston
Huntington, Massachusetts

Ahmet Demirkaya is a Ph.D. candidate at Northeastern University’s Cognitive Systems Laboratory (CSL). He received his B.S. degree in Electrical and Electronics Engineering from Bilkent University in 2020 and his M.S. degree from the Electrical and Computer Engineering Department of Northeastern University in 2022. His research focuses on developing hybrid modeling frameworks that merge physics-based methods with neural networks to explore complex dynamic systems, particularly those with partially observed states. Additionally, he applies machine learning to time series data for forecasting applications, such as utilizing wearable biosensing technology to predict aggressive behavior in autistic youth.

Publications

2023

Wearable biosensing to predict imminent aggressive behavior in psychiatric inpatient youths with autism

Published JAMA

Tales Imbiriba, Ahmet Demirkaya, Ashutosh Singh, and 2 more authors

JAMA network open, 2023

Abs HTML

IMPORTANCE Aggressive behavior is a prevalent and challenging issue in individuals with autism. OBJECTIVE To investigate whether changes in peripheral physiology recorded by a wearable biosensor and machine learning can be used to predict imminent aggressive behavior before it occurs in inpatient youths with autism. DESIGN, SETTING, AND PARTICIPANTS This noninterventional prognostic study used data collected from March 2019 to March 2020 from 4 primary care psychiatric inpatient hospitals. Enrolled participants were 86 psychiatric inpatients with confirmed diagnoses of autism exhibiting operationally defined self-injurious behavior, emotion dysregulation, or aggression toward others; 16 individuals were not included (18.6%) because they would not wear the biosensor (8 individuals) or were discharged before an observation could be made (8 individuals). Data were analyzed from March 2020 through October 2023. MAIN OUTCOMES AND MEASURES Research staff performed live behavioral coding of aggressive behavior while inpatient study participants wore a commercially available biosensor that recorded peripheral physiological signals (cardiovascular activity, electrodermal activity, and motion). Logistic regression, support vector machines, neural networks, and domain adaptation were used to analyze time-series features extracted from biosensor data. Area under the receiver operating characteristic curve (AUROC) values were used to evaluate the performance of population- and persondependent models.