AKBILGIC, OGUZ

Assistant Professor
Pediatrics-CBMI
 
Assistant Professor
Preventive Medicine

Office: 490R LE BONHEUR RESEARCH CENTER
50 NORTH DUNLAP STREET
MEMPHIS TN 381032893
Tel: (901) 287-5841
oakbilg1@uthsc.edu

Education

  • 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 Keywords

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

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

Publications

  1. R Mahajan, T Viangteeravat, O Akbilgic. Boosting the performance of symbolic pattern recognition by feature selection: A case study on detecting cardiac abnormalities. Artificial Intelligence in Medicine Conference, December 12-15, 2016, Dana point, CA.
  2. R. Mahajan, T. Viangteeravat, O. Akbilgic. Detection 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.
  3. R. Mahajan, R. Kamaleswaran, Oguz Akbilgic. Paroxysmal 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.
  4. VW Franco, A Khojandi, R Kamaleswaran, O Akbilgic, S nemati, RL Davis. 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.
  5. M.R. Langham Jr, O. Akbilgic, E. Huang, T. Jones, A. Walter, R.L. Davis. A 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.
  6. Mahajan, R, Viangteeravat, T, Akbilgic, O. Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics. Int J Med Inform, 108, 55-63, 2017.
  7. R. Mahajan, R. Kamaleswaran, O. Akbilgic. Effects of varying sampling frequency on the analysis of continuous ECG data streams. Lecture Notes in Computer Science series, Springer, 10494, 2017.
  8. Eduard Cubi, Joule Bergerson, Oguz Akbilgic. Grid Compensation Scores. Assessing the impact of buildings on the electricity grid. ISIE-ISSST 2017: Science in Support of Sustainable and Resilient Communities, 2017.
  9. E Cubi, O Akbilgic, J Bergerson. An assessment framework to quantify the interaction between the built environment and the electricity grid. Applied Energy, 206, 22-31, 2017.
  10. O Akbilgic, LR Langham Jr, RL Davis. Race, Preoperative Risk Factors, and Death after Surgery. Pediatrics (In Press), 2017.
  11. A Gaipov, MZ Molnar, PK Potukuchi, K Sumida, RB Canada, O Akbilgic, K Kabulbayev, K Kalantar-Zadeh, CP Kovesdy. Pre ESRD Coronary Artery Revascularization and Post ESRD Mortality. J Am Soc Nephrol, 2017 (28), 2, 2017.
  12. A Gaipov, MZ Molnar, PK Potukuchi, K Sumida, O Akbilgic, E Streja, C Rhee, RB Canada, K Kalantar-Zadeh, CP Kovesdy. AKI Following CABG versus PCI in Advanced CKD Patients. J Am Soc Nephrol, 2017 (28), 417, 2017.
  13. GG Gyamlani,PK Potukuchi, O Akbilgic, M Soohoo, E Streja, K Sumida, K Kalantar-Zadeh, MZ Molnar, CP Kovesdy. Vancomycin-Associated AKI. J Am Soc Nephrol, 2017 (28), 420, 2017.
  14. O. Akbilgic, J.A. Howe. Symbolic Pattern Recognition for Sequential Data. Sequential Analysis, 36 (4), 1-13, 2017.
  15. Oguz Akbilgic, Max R Langham Jr; Robert L Davis. Visualization 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.
  16. T Viangteeravat, O Akbilgic, RL Davis. Analyzing 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.
  17. A. Chan, D. McKean, O. Akbilgic, W. Smith. Investigating The Impact of Demographic Features on Body Size Discrimination. Journal of Vision, 17 (10), 517, 2017.
  18. O. Akbilgic, A.J. Howe, R.L. Davis. Categorizing Atrial Fibrillation via Symbolic Pattern Recognition. Journal of Medical Statistics and Informatics, 4 (8), 2016.
  19. T. Viangteeravat, V.R. Nagisetty, O. Akbilgic, F. Sen, R. Mudunuri, O. Ajayi, R.L. Davis. 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.
  20. O. Akbilgic, R.L. Davis. Searching for Fingerprints of Paroxysmal Atrial Fibrialtion: A Symbolic Pattern Recognition Approach. Southern Regional Council On Statistics Summer Research Conference, 2016.
  21. M.R. Asoglu, T. Achjian, O. Akbilgic, M.A Borahay, G. Kilic. The impact of a simulation training lab on outcomes of hysterectomy. Journal of Turkish-German Gynecological Association (17), 60-64, 2016.
  22. O. Akbilgic, M. Mahmoudkhan, G. Doluweera, J.A. Bergerson. A meta-analysis and pre- dictive analysis of CO2 avoided costs for Carbon Capture investment decisions in power plants. Applied Energy, 159, 11-18, 2015.
  23. P. Humez; B. Mayer; Jenifer Ing; M. Nightingale, V. Becker, A. Kingston, O. Akbilgic, S. Taylor. Occurrence and origin of methane in groundwater in Alberta (Canada): gas geochemical and isotopic approaches. Science of the Total Environment, 541, 1253--1268, 2015.
  24. O. Akbilgic, D. Zhu, I.D. Gates, J.A. Bergerson. Prediction of steam-assisted gravity drainage steam to oil ratio from reservoir characteristics. Energy, 93 (2), 1663–1670, 2015.
  25. O. Akbilgic. Classification Trees Aided Mixed Regression Model. Journal of Applied Statistics, 42 (8), 1773-1781, 2015.
  26. O. Akbilgic, H. Bozdogan, M.E. Balaban. A novel Hybrid RBF Neural Networks model as a forecaster. Statistics & Computing, 24 (3), 365-375, 2014.
  27. O. Akbilgic, D. Zhu, I.D. Gates, J.A. Bergerson. Prediction of Canadaâs oil sands GHG emissions: statistical model selection & evaluation. International Conference on Environmental Scinece & Technologies, 232-234, 2014.
  28. O. Akbilgic. Binary 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, 2013.
  29. O. Akbilgic. 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.
  30. H. Bozdogan, O. Akbilgic. Social network analysis of scientific collaborations across different subject fields. Information Services and Use, 33 (3-4), 219-233, 2013.
  31. O. Akbilgic. A 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,, 2013.
  32. O. Akbilgic, H. Bozdogan. Hybrid RBF Neural Network Models for Supervised Classification of Medical Data With Information Complexity And The Genetic Algorithm. National Biostatistics Conference, 2012.
  33. J.A. Howe, O. Akbilgic, E. Deniz Howe. Identifying the Presence of Outliers in Regression Using LSR. 7th International Statistics Congress,, 178-179, 2011.
  34. O. Akbilgic, H. Bozdogan. Predictive 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, 2011.
  35. O. Akbilgic, J.A. Howe. A Novel Normality Test Using an Identity Transformation of the Gaussian Function. European Journal of Pure and Applied Mathematics, 4 (4), 448-454, 2011.
  36. E. Deniz, O. Akbilgic, J.A. Howe. Model selection using information criteria under a new estimation method: least squares ratio. Journal of Applied Statistics, 38 (9), 2043-2050, 2011.
  37. Akinci, OF, Kurt, M, Terzi, A, Atak, I, Subasi, IE, Akbilgic, O. Natal cleft deeper in patients with pilonidal sinus: implications for choice of surgical procedure. Dis Colon Rectum, 52 (5), 1000-2, 2009.
  38. O. Akbilgic, E. Deniz Akinci. A Novel Regression Approach: Least Squares Ratio. Communications in Statistics - Theory and Methods, 38 (9), 1539-1545, 2009.
  39. O. Akbilgic, T. Keskinturk. The Comparison of Artificial Neural Networks and Regression Analysis. YONETIM, 60 (19), 74-83, 2008.
  40. F. Lorcu, O. Akbilgic. Canonical Correlation Analysis of Economical Indicators of OECD Countries. 2008 meeting of the International Conference on Business Management and Economics, 2008.
  41. Demirkok, SS, Basaranoglu, M, Akbilgic, O. Seasonal variation of the onset of presentations in stage 1 sarcoidosis. Int J Clin Pract, 60 (11), 1443-50, 2006.
  42. Demirkok SS, Basaranoglu M, Bilir M, Akbilgic O, Karayel T. Seasonal variation of the onset of presentations in patients with sarcoidosis presented with BHL alone. 2005 September 17; Copenhagen, Denmark, 2005.