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
www.akbilgic.com

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 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. 2018O Akbilgic, MR Langham, RL Davis,  Race, Preoperative Risk Factors, and Death After Surgery. Pediatrics 2018 Feb;141(2). pii: e20172221. doi: 10.1542/peds.2017-2221
  2.  2018O Akbilgic, MR Langham, AI Walter, TL Jones, EY Huang, RL Davis, A novel risk classification system for 30-day mortality in children undergoing surgery. PLoS One, 13 (1), e0191176
  3. 2018, R kamaleswaran, O Akbilgic, MA Hallman, RL Davis, SH Shah,  Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the Pediatric Intensive Care Unit. Pediatric Critical Care Medicine, PMID: 30052552, DOI: 10.1097/PCC.000000000001666.
  4. 2018, E Shin, R Mahajan, O Akbilgic, A Shaban-Nejad, Sociomarkers vs Biomarkers: Predictive Modeling in Identifying Pediatric Asthma Patients at Risk of Hospital Revisiting. Nature Digital Medicine, (2018) 1:50 ; doi:10.1038/s41746-018-0056-y
  5.  2018, R Kamaleswaran, R Mahajan, O Akbilgic,A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using varying length single lead electrocardiogram. Physiologic Measurement.
  6. 2018, J Sutton, R Mahajan, O Akbilgic, R Kamaleswaran, PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological Waveform. Journal of Biomedical Health Informatics (10.1109/JBHI.2018.2832610)
  7. 2018, 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, Acute Kidney Injury Following Coronary Revascularization in Patients with Advanced CKD, Nephrology Dialysis Transplant, gfy178, https://doi.org/10.1093/ntd/gfy178.
  8. 2018, A Gaipov, MZ Molnar, PK Potukuchi, K Sumida, RB Canada, O Akbilgic, K Kabulbayev, K Kalantar-Zadeh, CP Kovesdy, Pre-Dialysis Coronary Revascularization and post-Dialysis Mortality. Thoracic and Cardiovascular Surgery (In Press)
  9. 2018, R Mahajan, O Akbilgic, N Shafi, RL Davis, R Kamaleswaran, Machine learning applied to physiological data can predict fever onset in critically ill children, Pediatrics (In Review)
  10.  2018, R Mahajan, R Kamaleswaran, O Akbilgic,An approach ofr interpretable deep-learning: An ECG based comparative study of feature engineering with convolutional neural networks, IEEE Transactions in Neural Networks and Learning Systems (In Review) 
  11. 2018O Akbilgic, R Homayouni, K Heindrich, MR Langham, RL Davis, Unstructured text improves prediction of death after surgery in children. Health Informatics Journal (In Review)
  12. 2017O Akbilgic, J.A. Howe, Symbolic Pattern Recognition for Sequential Data. Sequential Analysis, 36 (4), 528-540
  13. 2017, R Mahajan, T Viangteeravat, O Akbilgic, Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metricsIEEE International Journal of Medical Informatics, 108, 55-63, 2017
  14. 2017, 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
  15. 2016O Akbilgic, A.J. Howe, R.L. Davis, Categorizing Atrial Fibrillation via Symbolic Pattern Recognition. Journal of Medical Statistics and Informatics, 4 (8)
  16. 2016, 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
  17. 2015P. 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.
  18. 2015O. 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
  19. 2015O 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
  20. 2015O Akbilgic, Classification Trees Aided Mixed Regression Model, Journal of Applied Statistics, 42 (8), 1773-1781, 2015
  21. 2014, O Akbilgic, H. Bozdogan, M.E. Balaban, A novel Hybrid RBF Neural Networks model as a forecaster. Statistics & Computing, 24 (3), 365-375
  22. 2013O 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
  23. 2013H. Bozdogan, O Akbilgic, Social network analysis of scientific collaborations across different subject fields. Information Services and Use, 33 (3-4), 219-233
  24. 2011O 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
  25. 2011E. 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
  26. 2011O 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
  27. 2009Akinci, OF, Kurt, M, Terzi, A, Atak, I, Subasi, IE, O Akbilgic, Natal cleft deeper in patients with pilonidal sinus: implications for choice of surgical procedure. Disease of Colon&Rectum, 52 (5), 1000-2
  28. 2009O Akbilgic, E. Deniz Akinci, A Novel Regression Approach: Least Squares Ratio. Communications in Statistics - Theory and Methods, 38 (9), 1539-1545
  29. 2008O Akbilgic, T. Keskinturk, The Comparison of Artificial Neural Networks and Regression Analysis. Yonetim, 60 (19), 74-83
  30. 2006SS Demirkok, M Basaranoglu, O Akbilgic, Seasonal 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. 2018R Mahajan, R Kamaleswaran and O Akbilgic, 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. 2018EK Shin, R Mahajan, O Akbilgic, A Shaban-Nejad, Identifying Sociomarkers of Pediatric Asthma Patients at Risk of Hospital Revisiting. Online Journal of Public Health Informatics, Vol 10 (1): e135, 2018.
  3. 2017A Chan, D McKean, O Akbilgic, W Smith, Investigating The Impact of Demographic Features on Body Size Discrimination. Journal of Vision, 17 (10), 517.
  4. 2017R Mahajan, R Kamaleswaran, JA Howe, O Akbilgic, Cardiac Rhythm Classification from a Short Single Lead ECG Recording via Random Forests. Computing in Cardiology, 44, 1-4, 2017
  5. 2017GG 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.
  6. 2017R. 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.
  7. 2017A 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
  8. 2017T 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.
  9. 2017A 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
  10. 2013O Akbilgic, H Bozdogan, 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

Conference Abstract/Posters

 

  1. 2018R Mahajan, E Shin, A Shaban-Nejad, RL Davis, O Akbilgic, 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. 2018, R Mahajan, O Akbilgic, N Shafi, RL Davis, R Kamaleswaran, Heart Rate Features Can Predict Fever Onset in Critically Ill Children. Le Bonheur Children''s Hospital Research Conference, March 28, 2018, 54, 2018
  3. 2018, 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. 2018, 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. 2018, 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. 2017O Akbilgic, MR Langham Jr, RL 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
  7. 2017, E Cubi, J Bergerson, O 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
  8. 2017, 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.
  9. 2017R. 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.
  10. 2017R. Mahajan, R. Kamaleswaran, O 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.
  11. 2016M.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.
  12. 2016R Mahajan, T Viangteeravat, O Akbilgic, Boosting 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. 2016O. 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
  14. 2016T. 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
  15. 2014O. 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
  16. 2013O. 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
  17. 2012, 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
  18. 2011J.A. Howe, O. Akbilgic, E. Deniz Howe, Identifying the Presence of Outliers in Regression Using LSR. 7th International Statistics Congress, 178-179, 2011
  19. 2008F. Lorcu, O. Akbilgic, Canonical Correlation Analysis of Economical Indicators of OECD Countries. 2008 meeting of the International Conference on Business Management and Economics, 2008.
  20. 2005SS Demirkok, M Basaranoglu, M Bilir, O Akbilgic, T Karayel, Seasonal 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