AI/machine learning for predictive modeling

Relevant publications

1. J. Tao, R.G. Larson, Y. Mintz, O. Alagoz, K. Hoppe (2024), “Predictive Modeling of Hypertension-Related Postpartum Readmission: Retrospective cohort analysis,” JMIR AI, 3:e48588, doi: 10.2196/48588.

2. V. Vahdat, O. Alagoz, J.V. Chen, L. Saoud, B.J. Borah, P.J. Limburg (2023), “Calibration and Validation of Colorectal Cancer and Adenoma Incidence & Mortality (CRC-AIM) Microsimulation Model using Deep Neural Networks,” Medical Decision Making, 43(6):719-736.

3. O. Alagoz, S. Srinivasan, I. Kim, and M. Kurt (2022). "MSR38 Can Logistic Regression (LOR) Better Predict the Significance of the Overall Survival (OS) from Surrogate Endpoints (SES) for Randomized Controlled Trials (RCTS) in Oncology? Insights From a Cross-Tumor Case Study." Value in Health 25(12): S357.  Poster presentation at ISPOR-Europe Annual Conference, November 6-9, 2022, Vienna, Austria, and virtual. 

4. N. Mukhtarova, O. Alagoz, Y. Chen*, K. Hoppe (2021), “Evaluation of different blood pressure assessment strategies and cutoff values to predict postpartum hypertension-related readmissions: A retrospective cohort study,” AJOG Maternal Fetal Medicine, 3(1):100252.

5. E. Scaria*, W.R. Powell, J. Birstler, O. Alagoz, D. Shirley, A.J.H. Kind, N. Safdar, (2020), “Neighborhood disadvantage and 30-day readmission risk following Clostridioides difficile infection hospitalization,” BMC Infectious Diseases,20(1):1-10.

6. J. Oruongo*, K. Rong, O. Alagoz, M. Smith (2020), “Skilled Nursing Facility Differences in Readmission Rates by the DRG Category of the Initial Hospitalization,” Journal of the American Medical Directors Association, 21(8):1175-1177.

7. M. Ayvaci*, O. Alagoz, J. Chhatwal*, A. Munoz del Rio, E.A. Sickles, H. Nassif, K. Kerlikowske, and E. S. Burnside (2014), “Predicting Invasive Breast Cancer versus DCIS in Different Age Groups,” BMC Cancer, 14 (584), https://doi.org/10.1186/1471-2407-14-584.

8. Y. Wu*, O. Alagoz, M. Ayvaci*, A. M. del Rio, D. Vanness, R. Woods, E.S. Burnside (2013), "A Comprehensive Methodology for Determining the Most Informative Mammographic Features," Journal of Digital Imaging, 26(5):941-947.

9. T. Ayer*, O. Alagoz, J. Chhatwal*, J. Shavlik, E.S. Burnside, and C.E. Kahn (2010), “Breast Cancer Risk Estimation with Artificial Neural Networks Revisited: Discrimination and Calibration,” Cancer, 116(14):3310-3321.

10. T. Ayer*, J. Chhatwal*, O. Alagoz, E.S. Burnside, and C.E. Kahn (2010), “Informatics in Radiology: Comparison of Logistic Regression and Artificial Neural Network Models in Breast Cancer Risk Estimation,”  Radiographics, 30(1):13-22.

11. J. Chhatwal*, O. Alagoz, M. J. Lindstrom, C. E. Kahn, K. A. Shaffer and E. S. Burnside (2009), “A Logistic Regression Model Based on the National Mammography Database Format to Aid Breast Cancer Diagnosis,” American Journal of Roentgenology, 192(4):1117-1127.

12. E. S. Burnside, J. Davis, J. Chhatwal*, O. Alagoz, M. J. Lindstrom, B. M. Geller, B. Littenberg, C. E. Kahn, K. A. Shaffer, and C. D. Page (2009), “Probabilistic Computer Model Developed from Clinical Data in the National Mammography Database Format to Classify Mammographic Findings,” Radiology, 251(3):663-672.