Assistant Professor
Assistant Professor
Preventive Medicine

MEMPHIS TN 381032893
Tel: (901) 287-5841


  • PostDoc, University of Calgary, Statistics
  • PostDoc, University of Tennessee, Knoxville, Statistics
  • Ph.D., Istanbul University, Quantitative Methods
  • M.S., Mimar Sinan University, Statistics
  • B.S., Istanbul University, Mathematics

Research Interest/Specialty

Statistical modeling, machine learning, and predictive modeling for clinical decision making.

* Cardiac abnormality detection and prediction

* Early detection of sepsis, MODS, respiratory failure on real-time physiological data streams

* RIsk stratification for adverse surgery outcome

* Identifying the source of racial disparities in surgery outcome by analysis of free text data in EMR

* Method development in statistics and machine learning

Journal Articles

  1. O Akbilgic, MR Langham, RL Davis, 2018Race, Preoperative Risk Factors, and Death After Surgery. Pediatrics 2018 Feb;141(2). pii: e20172221. doi: 10.1542/peds.2017-2221
  2. O Akbilgic, MR Langham, AI Walter, TL Jones, EY Huang, RL Davis, 2018A novel risk classification system for 30-day mortality in children undergoing surgery. PLoS One, 13 (1), e0191176
  3. R Kamaleswaran, R Mahajan, O Akbilgic, 2018A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using varying length single lead electrocardiogram. Physiologic Measurement, 2018
  4. J Sutton, R Mahajan, O Akbilgic, R Kamaleswaran, 2018PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological Waveform. Journal of Biomedical Health Informatics (10.1109/JBHI.2018.2832610)
  5. R kamaleswaran, O Akbilgic, MA Hallman, RL Davis, SH Shah, 2018, Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the Pediatric Intensive Care Unit. Pediatric Critical Care Medicine (In Press)
  6. A Gaipov, MZ Molnar, PK Potukuchi, K Sumida, Z Szabo, O Akbilgic, E Streja, Cm Rhee, S Koshy, RB Canada, K Kalantar-Zadeh, CP Kovesdy, 2018, Acute Kidney Injury Following Coronary Revascularization in Patients with Advanced CKD, Nephrology DIalysis Transplant (In Press).
  7. R Mahajan, O Akbilgic, N Shafi, RL Davis, R Kamaleswaran, 2018, Machine learning applied to physiological data can predict fever onset in critically ill children, Pediatrics (In Review)
  8. O Akbilgic, R Homayouni, K Heindrich, MR Langham, RL Davis, 2018, Unstructured text improves prediction of death after surgery in children. Nature Digital Medicine (In Review)
  9. E Shin, R Mahajan, O Akbilgic, A Shaban-Nejad, 2018, Sociomarkers vs Biomarkers: Predictive Modeling in Identifying Pediatric Asthma Patients at Risk of Hospital Revisiting. Nature Digital Medicine (In Review)
  10. O Akbilgic, J.A. Howe, 2017, Symbolic Pattern Recognition for Sequential Data. Sequential Analysis, 36 (4), 528-540
  11. R Mahajan, T Viangteeravat, O Akbilgic, 2017Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics. IEEE International Journal of Medical Informatics, 108, 55-63, 2017
  12. E Cubi, O Akbilgic, J Bergerson, 2017An assessment framework to quantify the interaction between the built environment and the electricity grid. Applied Energy, 206, 22-31
  13. O Akbilgic, A.J. Howe, R.L. Davis, 2016Categorizing Atrial Fibrillation via Symbolic Pattern Recognition. Journal of Medical Statistics and Informatics, 4 (8)
  14. M.R. Asoglu, T. Achjian, O Akbilgic, M.A Borahay, G. Kilic, 2016The impact of a simulation training lab on outcomes of hysterectomy. Journal of Turkish-German Gynecological Association (17), 60-64, 2016
  15. P. Humez; B. Mayer; Jenifer Ing; M. Nightingale, V. Becker, A. Kingston, O Akbilgic, S. Taylor, 2015Occurrence and origin of methane in groundwater in Alberta (Canada): gas geochemical and isotopic approaches. Science of the Total Environment, 541, 1253--1268, 2015.
  16. O. Akbilgic, D. Zhu, I.D. Gates, J.A. Bergerson, 2015Prediction of steam-assisted gravity drainage steam to oil ratio from reservoir characteristics. Energy, 93 (2), 1663–1670
  17. O Akbilgic, M. Mahmoudkhan, G. Doluweera, J.A. Bergerson, 2015A meta-analysis and pre- dictive analysis of CO2 avoided costs for Carbon Capture investment decisions in power plants. Applied Energy, 159, 11-18
  18. O Akbilgic, 2015Classification Trees Aided Mixed Regression Model, Journal of Applied Statistics, 42 (8), 1773-1781, 2015
  19. O Akbilgic, H. Bozdogan, M.E. Balaban, 2014, A novel Hybrid RBF Neural Networks model as a forecaster. Statistics & Computing, 24 (3), 365-375
  20. O Akbilgic, 2013Binary Classification for Hydraulic Fracturing Operations in Oil & GasWells via Tree Based Logistic RBF Networks. European Journal of Pure and Applied Mathematics, 6 (4), 377-386
  21. H. Bozdogan, O Akbilgic, 2013Social network analysis of scientific collaborations across different subject fields. Information Services and Use, 33 (3-4), 219-233
  22. O Akbilgic, H. Bozdogan, 2011Predictive Subset Selection using Regression Trees and RBF Neural Networks Hybridized with the Genetic Algorithm. European Journal of Pure and Applied Mathematics, 4 (4), 467-485
  23. E. Deniz, O Akbilgic, J.A. Howe, 2011Model selection using information criteria under a new estimation method: least squares ratio. Journal of Applied Statistics, 38 (9), 2043-2050
  24. O Akbilgic, J.A. Howe, 2011A Novel Normality Test Using an Identity Transformation of the Gaussian Function. European Journal of Pure and Applied Mathematics, 4 (4), 448-454
  25. Akinci, OF, Kurt, M, Terzi, A, Atak, I, Subasi, IE, O Akbilgic, 2009Natal cleft deeper in patients with pilonidal sinus: implications for choice of surgical procedure. Disease of Colon&Rectum, 52 (5), 1000-2
  26. O Akbilgic, E. Deniz Akinci, 2009A Novel Regression Approach: Least Squares Ratio. Communications in Statistics - Theory and Methods, 38 (9), 1539-1545
  27. O Akbilgic, T. Keskinturk, 2008The Comparison of Artificial Neural Networks and Regression Analysis. Yonetim, 60 (19), 74-83
  28. SS Demirkok, M Basaranoglu, O Akbilgic, 2006Seasonal variation of the onset of presentations in stage 1 sarcoidosis. International Journal of Clinical Practice, 60 (11), 1443-50

Conference Full Paper/Abstracts Published in Journals


  1. R Mahajan, R Kamaleswaran and O Akbilgic, 2018, A hybrid feature extraction method to detect Atrial Fibrillation from single lead ECG recording, 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Las Vegas, NV, 2018, pp. 116-119. doi: 10.1109/BHI.2018.8333383
  2. EK Shin, R Mahajan, O Akbilgic, A Shaban-Nejad, 2018, Identifying Sociomarkers of Pediatric Asthma Patients at Risk of Hospital Revisiting. Online Journal of Public Health Informatics, Vol 10 (1): e135, 2018.
  3. A Chan, D McKean, O Akbilgic, W Smith, 2017Investigating The Impact of Demographic Features on Body Size Discrimination. Journal of Vision, 17 (10), 517.
  4. R Mahajan, R Kamaleswaran, JA Howe, O Akbilgic, 2017Cardiac Rhythm Classification from a Short Single Lead ECG Recording via Random Forests. Computing in Cardiology, 44, 1-4, 2017
  5. GG Gyamlani,PK Potukuchi, O Akbilgic, M Soohoo, E Streja, K Sumida, K Kalantar-Zadeh, MZ Molnar, CP Kovesdy, 2017Vancomycin-Associated AKI. J Am Soc Nephrol, 2017 (28), 420, 2017.
  6. R. Mahajan, R. Kamaleswaran, O. Akbilgic, 2017Effects of varying sampling frequency on the analysis of continuous ECG data streams. Lecture Notes in Computer Science series, Springer, 10494, 2017.
  7. A Gaipov, MZ Molnar, PK Potukuchi, K Sumida, RB Canada, O Akbilgic, K Kabulbayev, K Kalantar-Zadeh, CP Kovesdy, 2017, Pre ESRD Coronary Artery Revascularization and Post ESRD Mortality. J Am Soc Nephrol, 2017 (28), 2, 2017
  8. T Viangteeravat, O Akbilgic, RL Davis, 2017Analyzing Electronic Medical Records to Predict Risk of DIT (Death, Intubation, or Transfer to ICU) in Pediatric Respiratory Failure or Related Conditions. AMIA Jt Summits Transl Sci Proc, 2017, 287-294, 2017.
  9. A Gaipov, MZ Molnar, PK Potukuchi, K Sumida, O Akbilgic, E Streja, C Rhee, RB Canada, K Kalantar-Zadeh, CP Kovesdy, 2017AKI Following CABG versus PCI in Advanced CKD Patients. J Am Soc Nephrol, 2017 (28), 417, 2017
  10. O Akbilgic, H Bozdogan, 2013A New Supervised Classification of Credit Approval Data via The Hybrid RBF-NN Model Using The Genetic Algorithm with Information Complexity. Data Science, Learning by Latent Structures, and Knowledge Discovery

Conference Abstract/Posters


  1. R Mahajan, E Shin, A Shaban-Nejad, RL Davis, O Akbilgic, 2018, Analyzing Correlations between population-level socioeconomic markers and patient-level clinical risk factors for adverse pediatric surgery outcome. Southeastern Pediatric Research Conference: Precision Medicine, Atlanta, GA, June 8, 2018.
  2. R Mahajan, O Akbilgic, N Shafi, RL Davis, R Kamaleswaran, 2018Heart Rate Features Can Predict Fever Onset in Critically Ill Children. Le Bonheur Children''s Hospital Research Conference, March 28, 2018, 54, 2018
  3. EK Shin, R Mahajan, O Akbilgic, A Shaban-Nejad, 2018Identifying Sociomarkers of Pediatric Asthma Patients at Risk of Hospital RevisitingLe Bonheur Children''s Hospital Research Conference, March 28, 2018, 53, 2018.
  4. O Akbilgic, R Homayouni, K Heindrich, MR Langham, RL Davis, 2018Text data in EMRs has a lotto say about surgery outcomeLe Bonheur Children''s Hospital Research Conference, March 28, 2018, 9, 25, 2018
  5. EK Shin, R Mahajan, O Akbilgic, A Shaban-Nejad, 2018Bringing the Socio-markers into Health Surveillance: A Predictive Model for Pediatric Asthma Patients at Risk of Hospital RevisitingMedical Informatics Europe 2018, 2018
  6. O Akbilgic, MR Langham Jr, RL Davis, 2017Visualization of Racial Disparities in Surgical Outcomes among Children via Network Analysis of Pre-Operative Risk Factors. Southern Pediatric Research Conference: Big Data for Better Care, June 9, 2017
  7. E Cubi, J Bergerson, O Akbilgic, 2017Grid Compensation Scores. Assessing the impact of buildings on the electricity grid. ISIE-ISSST 2017: Science in Support of Sustainable and Resilient Communities, 2017
  8. VW Franco, A Khojandi, R Kamaleswaran, O Akbilgic, S nemati, RL Davis, 2017 How Much Data Should We Collect? a Case Study in Sepsis Detection Using Deep Learning. IEEE-NIH 2017 Special Topics Conference on Healthcare Innovations and Point-of-Care Technologies. Nov 6-8, 2017, MD, USA.
  9. R. Mahajan, T. Viangteeravat, O Akbilgic, 2017Detection of Congestive Heart Failure Using R-R Interval Via Probabilistic Symbolic Pattern Recognition. 2017 IEEE International Conference on Biomedical and Health Informatics Feb. 16-19, 2017, Orlando, Florida, USA.
  10. R. Mahajan, R. Kamaleswaran, O Akbilgic, 2017Paroxysmal Atrial Fibrillation Screening at Different ECG Sampling Frequencies Using Probabilistic Symbolic Pattern Recognition. 2017 IEEE International Conference on Biomedical and Health Informatics, Feb. 16-19, 2017, Orlando, Florida, USA.
  11. M.R. Langham Jr, O. Akbilgic, E. Huang, T. Jones, A. Walter, R.L. Davis, 2016A Simple Decision Support Tool for Surgery in Children Utilizing NSQIP-Pediatric Data. Presented at ACS NSQIP 2016 Conference, July 16-17, 2016, San Diego, CA.
  12. R Mahajan, T Viangteeravat, O Akbilgic, 2016Boosting the performance of symbolic pattern recognition by feature selection: A case study on detecting cardiac abnormalitiesArtificial Intelligence in Medicine Conference, December 12-15, 2016, Dana point, CA.
  13. O. Akbilgic, R.L. Davis, 2016Searching for Fingerprints of Paroxysmal Atrial Fibrialtion: A Symbolic Pattern Recognition Approach. Southern Regional Council On Statistics Summer Research Conference, 2016
  14. T. Viangteeravat, V.R. Nagisetty, O. Akbilgic, F. Sen, R. Mudunuri, O. Ajayi, R.L. Davis, 2016. Predicting risk of respiratory decompensation or death for hospitalized children with asthma or related conditions using machine learning techniques. Poster presentation at Pediatric Academic Societies Meeting, April 30-May 3, 2016
  15. O. Akbilgic, D. Zhu, I.D. Gates, J.A. Bergerson, 2014Prediction of Canadaâs oil sands GHG emissions: statistical model selection & evaluation. International Conference on Environmental Scinece & Technologies, 232-234, 2014
  16. O. Akbilgic, 2013. Tree Based Logistic RBF Neural Networks Aided Logistic regression for Binary Classification: A Case Study on Hydraulic Fracturing in Oil & Gas Well. Presented at the yBIS 2013: Joint Meeting of Young Business and Industrial Statisticians, 2013
  17. O. Akbilgic, H. Bozdogan, 2012 Hybrid RBF Neural Network Models for Supervised Classification of Medical Data With Information Complexity And The Genetic Algorithm. National Biostatistics Conference, 2012
  18. J.A. Howe, O. Akbilgic, E. Deniz Howe, 2011Identifying the Presence of Outliers in Regression Using LSR. 7th International Statistics Congress, 178-179, 2011
  19. F. Lorcu, O. Akbilgic, 2008. Canonical Correlation Analysis of Economical Indicators of OECD Countries. 2008 meeting of the International Conference on Business Management and Economics, 2008.
  20. SS Demirkok, M Basaranoglu, M Bilir, O Akbilgic, T Karayel, 2005Seasonal variation of the onset of presentations in patients with sarcoidosis presented with BHL alone. ERS 2005 September 17; Copenhagen, Denmark, 2005.

Research Keywords

Predictive Modeling, Data Mining, Neural Networks, Statistics, Classification, Prediction, Clustering