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PUBLICATIONS

Journal Articles

  1. 2019, O Akbilgic, RL Davis, The Promise of Machine Learning: When will it be delivered?, Journal of Cardiac Failure, https://doi.org/10.1016/j.cardfail.2019.04.006, (PMID:30978508).
  2. 2019, O Akbilgic, R Homayouni, K Heindrich, MR Langham, RL Davis, Unstructured text improves prediction of death after surgery in children. Informatics: Special Issue on Data-Driven Health Informatics, 6(1): 4.
  3. 2019, R Kamaleswaran, O Akbilgic, M Hallman, A West, RL Davis, S Shah, Artificial Intelligence Towards an Intelligent Clinical Support, Pediatric Critical Care Medicine, Volume 20 (4), p 399, doi: 10.1097/PCC.0000000000001874.
  4. 2019, 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, Volume 157, Issue 3, March 2019, Pages 976-983.e7 DOI: https://doi.org/10.1016/j.jtcvs.2018.08.107
  5. 2019, G Gyamlani, P Potukuchi, F Thomas, O Akbilgic, M Soohoo, E Streja, A Naseer, K Sumida, MZ Molnar, K Kalantar-Zadeh, CP Kovesdy, Vancomycin-Associated Acute Kidney Injury in Large Veteran Population, American Journal of Nephrology, 49 (2), 133-142.
  6. 2019, J Sutton, R Mahajan, O Akbilgic, R Kamaleswaran, PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological Waveform. IEEE Journal of Biomedical Health Informatics 23 (1), 59-65.
  7. 2019, O Akbilgic, Y Obi, P Potukuchi, I Karabayir, N Nguyen, M Soohoo, E Streja, MZ Molnar, C Rhee, K Kalantar-Zadeh, CP Kovesdy, Identification of patients at high risk of death within one year of initiation of dialysis using machine learning, Kidney International Reports (In Review)
  8. 2019, I Karabayir, O Akbilgic, N Tas, A novel learning algorithm to optimize deep neural networks: Evolved Gradient Direction Optimizer (EVGO), IEEE Transactions in Neural Networks and Learning Systems (In Review)
  9. 2019, T Rawal, O Ali,  SH Liu, SV Pingali, O Akbilgic, H O’Neil, L Tetards, S Santra, L Petridis, Interaction of Zinc Oxide Nanoparticle with Water, ACS Applied Nano Materials (In Review).
  10. 2018, O 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
  11. 2018, O 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
  12. 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.
  13. 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
  14. 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, 39 (3), 035006.
  15. 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.
  16. 2018, R Mahajan, R Kamaleswaran, O Akbilgic, Differences and Overlaps of CNN vs Manually Extracted ECG Features: A Case Study on Arrhythmia Detection, METHODS of Information in Medicine (In Review) 
  17. 2017, O Akbilgic, J.A. Howe, Symbolic Pattern Recognition for Sequential Data. Sequential Analysis, 36 (4), 528-540
  18. 2017, R Mahajan, T Viangteeravat, O Akbilgic, Improved 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
  19. 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
  20. 2016, O Akbilgic, A.J. Howe, R.L. Davis, Categorizing Atrial Fibrillation via Symbolic Pattern Recognition. Journal of Medical Statistics and Informatics, 4 (8)
  21. 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
  22. 2015, 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.
  23. 2015, 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
  24. 2015, 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
  25. 2015, O Akbilgic, Classification Trees Aided Mixed Regression Model, Journal of Applied Statistics, 42 (8), 1773-1781, 2015
  26. 2014, O Akbilgic, H. Bozdogan, M.E. Balaban, A novel Hybrid RBF Neural Networks model as a forecaster. Statistics & Computing, 24 (3), 365-375
  27. 2013, 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
  28. 2013, H. Bozdogan, O Akbilgic, Social network analysis of scientific collaborations across different subject fields. Information Services and Use, 33 (3-4), 219-233
  29. 2011, 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
  30. 2011, 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
  31. 2011, 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
  32. 2009, Akinci, 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
  33. 2009, O Akbilgic, E. Deniz Akinci, A Novel Regression Approach: Least Squares Ratio. Communications in Statistics – Theory and Methods, 38 (9), 1539-1545
  34. 2008, O Akbilgic, T. Keskinturk, The Comparison of Artificial Neural Networks and Regression Analysis. Yonetim, 60 (19), 74-83
  35. 2006, SS 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. 2019, S Giri, JL Jefferies, F Thomas, RL Davis, O Akbilgic, Abnormalities in Normal Sinus Rhythm, Circulation: Cardiovascular Quality and Outcome, 12:A25, https://doi.org/10.1161/hcq.12.suppl_1.25.
  2. 2019, R Kamaleswaran, R Mahajan, O Akbilgic, N Shafi, RL Davis, Machine Learning Applied To Continuous Physiologic Data Predicts Fever In Critically Ill Children, Critical Care Medicine, Vol: 47 (1), pp. 23.
  3. 2018, R Mahajan, EK Shin, A Shaban-Nejad, MR Langham, M Martin, RL Davis, O Akbilgic, Disparities in Population-Level Socio-Economic Factors are Associated with Disparities in Preoperative Clinical Risk factors in Children, Stud Health Technol Inform. 2018;255:80-84, PMID: 30306911.
  4. 2018, R 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
  5. 2018, EK 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.
  6. 2018, R Kamaleswaran, O Akbilgic, M Hallman, A West, RL Davis, S Shah, Physiomarker Variability for Early Prediction of Severe Sepsis in the Pediatric Intensive Care Unit, Critical care Medicine, January 2018, 46 (1), p 745, doi: 10.1097/01.ccm.0000529525.72822.2e.
  7. 2017, A Chan, D McKean, O Akbilgic, W Smith, Investigating The Impact of Demographic Features on Body Size Discrimination. Journal of Vision, 17 (10), 517.
  8. 2017, R 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
  9. 2017, 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.
  10. 2017, 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.
  11. 2017, 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. 2017, 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.
  13. 2017, 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
  14. 2013, O 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. 2018, R 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, 2018. Identifying Sociomarkers of Pediatric Asthma Patients at Risk of Hospital Revisiting. Le Bonheur Children”s Hospital Research Conference, March 28, 2018, 53, 2018.
  4. 2018, O Akbilgic, R Homayouni, K Heindrich, MR Langham, RL Davis, 2018. Text data in EMRs has a lotto say about surgery outcome. Le Bonheur Children”s Hospital Research Conference, March 28, 2018, 9, 25, 2018
  5. 2018, EK Shin, R Mahajan, O Akbilgic, A Shaban-Nejad, 2018. Bringing the Socio-markers into Health Surveillance: A Predictive Model for Pediatric Asthma Patients at Risk of Hospital Revisiting. Medical Informatics Europe 2018, 2018
  6. 2017, O 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, 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.
  9. 2017, R. 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.
  10. 2016, 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.
  11. 2016, 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.
  12. 2016, 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
  13. 2016, 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
  14. 2014, 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
  15. 2013, 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
  16. 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
  17. 2011, J.A. Howe, O. Akbilgic, E. Deniz Howe, Identifying the Presence of Outliers in Regression Using LSR. 7th International Statistics Congress, 178-179, 2011
  18. 2008, 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.
  19. 2005, SS 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.