Associate Professor

Health Informatics and Data Science

Oguz Akbilgic
  • Dr. Akbilgic is a data scientist with expertise in statistical modeling, machine learning, and deep learning for clinical decision making. He earned his BS degree in Mathematics (2001) and PhD in Quantitative Methods (2011) from Istanbul University and MS in Statistics from Mimar SInan University (2005). He carried out postdoctoral studies at University of Tennessee Knoxville, (2012-2103) and University of Calgary (2013-2015). He worked at University of Tennessee Health Sciences Center, Memphis, TN as an Assistant Professor of Biomedical Informatics (2015-2019). He is currently an Associate Professot of Health Informatics at Loyola University Chicago, Maywood, IL.

    • University of Istanbul/Cerrahpasa Medical School, Ph.D.               

    What prompted you to pursue your field?

    Data science can be implemented in almost all disciplines. However, the self satisfaction a data science can get from helping saving lives, preventing diseases, and improving people's quality of live is priceless

    What does your department's focus mean to you?

    Health Informatics is the most useful, applicable, and needed way to use statistics, machine learning, and artificial intelligence.

    Why is this area of study important at this point in time?

    Health Informatics is a gateway to future Medicine and Health Care shaped by Artificial Intelligence.

    What would you tell a student about why your field is exciting/important/the potential impact s/he could make?

    AI based data science is not only used for testing hypothesis from existing literature but also help generating new hypothesizes to contribute to literature.


    • The quest for cardiovascular disease risk prediction models in patients with nondialysis chronic kidney disease. Streja, E; Norris, KC; Budoff, MJ; Hashemi, L; Akbilgic, O; Kalantar-Zadeh, K CURRENT OPINION IN NEPHROLOGY AND HYPERTENSION 2021 ;30(1)
    • A Data Science Approach to Analyze the Association of Socioeconomic and Environmental Conditions With Disparities in Pediatric Surgery. Akbilgic, O; Shin, EK; Shaban-Nejad, A Frontiers in pediatrics 2021 ;9
    • Biomedical Information Technology A Shaban-Nejad, R Kamaleswaran, EK Shin, O Akbilgic 2020
    • Network Analysis of Postoperative Surgical Complications in a Cohort of Children Reported to the National Surgical Quality Improvement Program: Pediatric. Alzubaidi, AN; Karabayir, I; Akbilgic, O; Langham, MR Annals of Surgery 2020
    • Discovery and predictive modeling of urine microbiome, metabolite and cytokine biomarkers in hospitalized patients with community acquired pneumonia. Pierre, JF; Akbilgic, O; Smallwood, H; Cao, X; Fitzpatrick, EA; Pena, S; Furmanek, SP; Ramirez, JA; Jonsson, CB Scientific reports 2020 ;10(1)
    • Electrocardiographic changes predate Parkinson's disease onset. Akbilgic, O; Kamaleswaran, R; Mohammed, A; Ross, GW; Masaki, K; Petrovitch, H; Tanner, CM; Davis, RL; Goldman, SM Scientific reports 2020 ;10(1)
    • The relation between lignin sequence and its 3D structure. Rawal, TB; Zahran, M; Dhital, B; Akbilgic, O; Petridis, L Biochimica et biophysica acta. General subjects 2020 ;1864(5)
    • The Application of Machine Learning to the Prediction of Postoperative Sepsis Following Appendectomy C. Bunn, S. Kulshrestha, S. Birch, J. Boyda, N. Balasubramanian, I. Karabayir, M. Baker, F. Luchette, F. Modave, O. Akbilgic Surgery 2020 ;7:045
    • Comparative Analysis Between Convolutional Neural Network Learned and Engineered Features: A Case Study on Cardiac Arrhythmia Detection R. Mahajan, R. Kamaleswaran, O. Akbilgic CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2020 ;1:37-44
    • Application of machine learning to the prediction of postoperative sepsis after appendectomy. Bunn, C; Kulshrestha, S; Boyda, J; Balasubramanian, N; Birch, S; Karabayir, I; Baker, M; Luchette, F; Modave, F; Akbilgic, O Surgery 2020
    • Gradient boosting for Parkinson's disease diagnosis from voice recordings. Karabayir, I; Goldman, SM; Pappu, S; Akbilgic, O BMC MEDICAL INFORMATICS AND DECISION MAKING 2020 ;20(1)
    • Network Analysis of Postoperative Surgical Complications in a Cohort of Children Reported to the National Surgical Quality Improvement Program: Pediatric AN Alzubaidi, I Karabayir, O Akbilgic, MR Langham Annals of Surgery 2020 ;Forhcoming
    • Machine learning analysis on American Gut Project microbiome data to identify subjects with cancer both with and without chemotherapy exposure. O Akbilgic, I Karabayir, H Gunturkun, JF Pierre, AC Rashe, A Thomas Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2020 ;38(15_suppl):e14069-e14069
    • Deep learning for improved prediction of late-onset cardiomyopathy among childhood cancer survivors: A report from the St. Jude Lifetime Cohort (SJLIFE). Fatma Gunturkun, Robert L Davis, Gregory T Armstrong, John L Jefferies, Kirsten K Ness, Daniel M Green, John Thomas Lucas, Deokumar Srivastava, Melissa M Hudson, Leslie L Robison, Daniel A Mulrooney, Elsayed Z Soliman, Ibrahim Karabayir, Oguz Akbilgic Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2020 ;38(15_supl):10545-10545
    • Abnormalities within Normal Sinus Rhythm Sadip Giri, John L Jefferies, Fridtjof Thomas, Robert L Davis, Oguz Akbilgic CIRCULATION JOURNAL 2019 ;12(Suppl_1):A25
    • Machine Learning to Identify Dialysis Patients at High Death Risk. Akbilgic, O; Obi, Y; Potukuchi, PK; Karabayir, I; Nguyen, DV; Soohoo, M; Streja, E; Molnar, MZ; Rhee, CM; Kalantar-Zadeh, K; Kovesdy, CP KIDNEY INTERNATIONAL REPORTS 2019 ;4(9)
    • Fungi form interkingdom microbial communities in the primordial human gut that develop with gestational age. Willis, KA; Purvis, JH; Myers, ED; Aziz, MM; Karabayir, I; Gomes, CK; Peters, BM; Akbilgic, O; Talati, AJ; Pierre, JF FASEB journal : official publication of the Federation of American Societies for Experimental Biology 2019 ;33(11)
    • Unstructured text can improve prediction of death after surgery in children O Akbilgic, R Homauyoni, K Heindrich, MR Langham, RL Davis INFORMATICS 2019 ;6(1):4
    • The Promise of Machine Learning: When Will it be Delivered? O Akbilgic, RL Davis JOURNAL CARDIOLOGY FAILURE 2019 ;25(4):454-5
    • Natal Cleft Deeper Patients with Pilonidal Sinus Implications for Choice of Surgical Procedure OF Akinci, M Kurt, A Terzi, I Atak, E Subasi, O Akbilgic Diseases of the Colon & Rectum 2019 ;38(9):74-83
    • Interaction of Zinc Oxide nanoparticles with Water: Implications for Catalytic Activity TB Rawal, A Ozcan, SH Liu, SV Pingali, O Akbilgic, L Tetard, H O’Neil, S Santra, L Petridis ACS nano 2019 ;2(7):4257-66
    • The Promise of Machine Learning: When Will it be Delivered? Akbilgic, O; Davis, RL Journal of cardiac failure 2019 ;25(6)
    • Artificial Intelligence: Progress Towards an Intelligent Clinical Support System. Kamaleswaran, R; Akbilgic, O; Hallman, MA; West, AN; Davis, RL; Shah, SH Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 2019 ;20(4)
    • Vancomycin-Associated Acute Kidney Injury in a Large Veteran Population. Gyamlani, G; Potukuchi, PK; Thomas, F; Akbilgic, O; Soohoo, M; Streja, E; Naseer, A; Sumida, K; Molnar, MZ; Kalantar-Zadeh, K; Kovesdy, CP American Journal of Nephrology 2019 ;49(2)
    • Disparities in Population-Level Socio-Economic Factors Are Associated with Disparities in Preoperative Clinical Risk Factors in Children. Mahajan, R; Shin, EK; Shaban-Nejad, A; Langham, MR; Martin, MY; Davis, RL; Akbilgic, O Studies in health technology and informatics 2018 ;255
    • Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU. Kamaleswaran, R; Akbilgic, O; Hallman, MA; West, AN; Davis, RL; Shah, SH Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 2018 ;19(10)
    • PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological Waveform. Sutton, JR; Mahajan, R; Akbilgic, O; Kamaleswaran, R IEEE journal of biomedical and health informatics 2018 ;23(1)
    • Acute kidney injury following coronary revascularization procedures in patients with advanced CKD. Gaipov, A; Molnar, MZ; Potukuchi, PK; Sumida, K; Szabo, Z; Akbilgic, O; Streja, E; Rhee, CM; Koshy, SKG; Canada, RB; Kalantar-Zadeh, K; Kovesdy, CP Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association 2018
    • A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length. Kamaleswaran, R; Mahajan, R; Akbilgic, O PHYSIOLOGICAL MEASUREMENT 2018 ;39(3)
    • A novel risk classification system for 30-day mortality in children undergoing surgery. Akbilgic, O; Langham, MR; Walter, AI; Jones, TL; Huang, EY; Davis, RL PLoS ONE 2018 ;13(1)
    • Race, Preoperative Risk Factors, and Death After Surgery. Akbilgic, O; Langham, MR; Davis, RL Pediatrics 2018 ;141(2)
    • Sociomarkers vs Biomarkers: Predictive Modeling in Identifying Pediatric Asthma Patients at Risk of Hospital Revisiting E. Shin, R. Mahajan, O. Akbilgic, A. Shaban-Nejad NATURE DIGITAL MEDICINE 2018 (1:50)
    • An Assessment Framework to Quantify the Interaction Between the Built Environment and the Electricity Grid E. Cubi, O. Akbilgic, J.A. Bergerson APPLIED ENERGY 2017 ;206:22-31
    • Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics. Mahajan, R; Viangteeravat, T; Akbilgic, O INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 2017 ;108
    • Analyzing Electronic Medical Records to Predict Risk of DIT (Death, Intubation, or Transfer to ICU) in Pediatric Respiratory Failure or Related Conditions. Viangteeravat, T; Akbilgic, O; Davis, RL AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science 2017 ;2017
    • The impact of a simulation-based training lab on outcomes of hysterectomy. Asoglu, MR; Achjian, T; Akbilgiç, O; Borahay, MA; Kiliç, GS Journal of the Turkish German Gynecological Association 2016 ;17(2)
    • Occurrence and Origin of Methane in Groundwater in Alberta (Canada): Gas Geochemical and Isotopic Approaches P. Humez, B. Mayer, J. Ing, M. Nightingale, V. Becker, A. Kingston, O. Akbilgic, S. Taylor SCIENCE OF THE TOTAL ENVIRONMENT 2016 (543):1253-1268
    • Categorizing Atrial Fibrillation via Symbolic Pattern Recognition O. Akbilgic, A.J. Howe, R.L. Davis JOURNAL OF MEDICAL STATISTIC AND INFORMATICS 2016 ;4(8):1-9
    • The Impact of Implementing a Simulation Training Laboratory in a Teaching Institution on the Surgical Outcomes of Hysterectomies M.R. Asoglu, T. Achjian, O. Akbilgic, M.A. Borahay, G. Kilic JOURNAL OF THE TURKISH-GERMAN GYNECOLOGICAL ASSOCIATION 2016 ;17:60-64
    • Classification Trees Aided Mixed Regression Model O. Akbilgic JOURNAL OF APPLIED STATISTICS 2015 ;42:1773-1781
    • A Meta-Analysis and Predictive Analysis of CO2 Avoided Costs for Carbon Capture Investment Decisions in Power Plants O. Akbilgic, M. Mahmoudkhan, G. Doluweera, J.A. Bergerson APPLIED ENERGY 2015 (159):11-18
    • Prediction of Steam to Oil Ratio of Oil Sands Wells Using Reservoir Characteristics O. Akbilgic, D. Xhu, I. Gates, J.A. Bergerson ENERGY 2015 :1663-1670
    • A Novel Hybrid RBF Neural Networks Model as a Forecaster O. Akbilgic, H. Bozdogan, M.E. Balaban STATISTICS AND COMPUTING 2014 ;24:365-375
    • Binary Classification of Hydraulic Fracturing in Oil and Gas Wells via Tree Based Logistic RBF Networks O. Akbilgic EUROPEAN JOURNAL OF PURE AND APPLIED MATHEMATICS 2013 ;6:377-386
    • Social Network Analysis of Scientific Collaborations Between Different Cross Disciplinary Fields H. Bozdogan, O. Akbilgic INFORMATION SERVICES & USE 2013 ;33:219-233
    • A Novel Normality Test Using Identity Transformation of Gaussian Function O. Akbilgic, J.A. Howe EUROPEAN JOURNAL OF PURE AND APPLIED MATHEMATICS 2011 ;4:448-454
    • Predictive Subset Selection Using Regression Trees and RBF Neural Networks Hybridized with The Genetic Algorithm O. Akbilgic, H. Bozdogan EUROPEAN JOURNAL OF PURE AND APPLIED MATHEMATICS 2011 ;4:467-485
    • Model Selection Using Information Criteria Under a New Estimation Method: Least Squares Ratio E. Deniz, O. Akbilgic, J.A. Howe JOURNAL OF APPLIED STATISTICS 2011 ;38:2043-2050
    • A Novel Regression Approach: Least Squares Ratio Communications in Statistics. O. Akbilgic, E. Deniz THEORY AND METHODS 2009 ;38:1539-1545
    • The Comparison of Artificial Neural Networks and Regression Analysis O. Akbilgic, t. Keskinturk ISTANBUL MANAGEMENT JOURNAL 2008 ;19:74-83
    • Seasonal Variation of The Onset of Stage 1 Sarcoidosis S Demirkok, M Basaranoglu, O Akbilgic International journal of clinical practice 2008 ;60:1443-50