AKBILGIC, OGUZ

Assistant Professor
Pediatrics
 
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

Predictive Modeling, Data Mining, Neural Networks, Statistics, Classification, Prediction, Clustering, Clinical Informatics, Clinical Decision Support Systems

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. O. Akbilgic, J.A. Howe. Symbolic Pattern Recognition for Sequential Data. Sequential Analysis (In Review).
  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. 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..
  5. 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.
  6. T Viangteeravat, O Akbilgic, RL Davis. Predictive modeling using data set from Electronic Medical Records to Predict Risk of Death, Intubation, or Transfer to ICU in Pediatric Respiratory Failure or Related Conditions. AMIA 2017 Joint Summits, 2017.
  7. 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.
  8. 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.
  9. O. Akbilgic, M.R. Langham, Jr, A.I. Walter, T. Jones, E.Y. Huang, R.L. Davis. Accurate Risk Classification for Death within30 Days Following Surgery in Children: Classification Trees Analysis of Risk Factors Utilizing National Surgical Quality Improvement Project-Pediatric Data (In Review). Surgery, 2016.
  10. O. Akbilgic, A.J. Howe, R.L. Davis. Categorizing Atrial Fibrillation via Symbolic Pattern Recognition. Journal of Medical Statistics and Informatics, 4 (8), 2016.
  11. 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.
  12. O. Akbilgic. Classification Trees Aided Mixed Regression Model. Journal of Applied Statistics, 42 (8), 1773-1781, 2015.
  13. 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.
  14. 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.
  15. 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.
  16. O. Akbilgic, H. Bozdogan, M.E. Balaban. A novel Hybrid RBF Neural Networks model as a forecaster. Statistics & Computing, 24 (3), 365-375, 2014.
  17. 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.
  18. 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.
  19. 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.
  20. H. Bozdogan, O. Akbilgic. Social network analysis of scientific collaborations across different subject fields. Information Services and Use, 33 (3-4), 219-233, 2013.
  21. 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.
  22. 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.
  23. J.A. Howe, O. Akbilgic, E. Deniz Howe. Identifying the Presence of Outliers in Regression Using LSR. 7th International Statistics Congress,, 178-179, 2011.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. O. Akbilgic, E. Deniz Akinci. A Novel Regression Approach: Least Squares Ratio. Communications in Statistics - Theory and Methods, 38 (9), 1539-1545, 2009.
  29. O. Akbilgic, T. Keskinturk. The Comparison of Artificial Neural Networks and Regression Analysis. Yönetim Dergisi, 19 (60), 74-83, 2008.
  30. O. Akbilgic, T. Keskinturk. The Comparison of Artificial Neural Networks and Regression Analysis. YONETIM, 60 (19), 74-83, 2008.
  31. 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.
  32. 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.
  33. 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.