O Ben-Assuli, T Heart, N Yin, R. Klempfner, R Padman. 2024. On Expert-Machine Partnership to Predict Mortality of Congestive Heart Failure Patients. Information Systems Management. https://doi.org/10.1080/10580530.2024.2312380.
M. Chen, X. Tan, R. Padman. 2023. A Machine Learning Approach to Support Urgent Stroke Triage Using Administrative Data and Social Determinants of Health at Hospital Presentation: Retrospective Study. J Med Internet Res., 2023 Jan 30;25:e36477. doi: 10.2196/36477.
Burstin, H., S. Curry, M. Ranney, V. Arora, B. Boxer Wachler, W.-Y. S. Chou, R. Correa, D. Cryer, D. Dizon, E. Flores, G. Harmon, A. Jain, K. Johnson, C. Laine, L. Leininger, G. McMahon, L. Michaelis, R. Minhas, R. Mularski, J. Oldham, R. Padman, C. Pinnock, J. Rivera, B. Southwell, A. Villarruel, and K. Wallace. 2023. Identifying Credible Sources of Health Information in Social Media: Phase 2—Considerations for Non-accredited Nonprofit Organizations, For-profit Entities, and Individual Sources. NAM Perspectives. Discussion Paper, National Academy of Medicine, Washington, DC.
G Shmueli, B Colosimo, D Martens, R Padman, M Saar-Tsechansky, O Sheng, WN Street, and K Tsui. 2023. How can IJDS authors, reviewers, and editors use (and misuse) generative AI?. IJDS Editorial, Issue #3 (June 2023).
O Ben-Assuli, T Heart, R. Klempfner, R Padman. 2023. Human-machine collaboration for feature selection and integration to improve congestive Heart failure risk prediction. Decision Support Systems, Vol. 172, 2023, 113982, ISSN 0167-9236, https://doi.org/10.1016/j.dss.2023.113982.
S Kumar, M Arnold, G James, R Padman. 2022. Developing a common data model approach for DISCOVER CKD: A retrospective, global cohort of real-world patients with chronic kidney disease. PLoS ONE 17(9): e0274131. https://doi.org/10.1371/journal.pone.0274131.
Reamer C, Chi WN, Gordon R, Sarswat N, Gupta C, Gaznabi S, White VanGompel E, Szum I, Morton-Jost M, Vaughn J, Larimer K, Victorson D, Erwin J, Halasyamani L, Solomonides A, Padman R, Shah NS. Continuous Remote Patient Monitoring in Patients With Heart Failure (Cascade Study): Protocol for a Mixed Methods Feasibility Study. JMIR Res Protoc. 2022 Aug 25;11(8):e36741. doi: 10.2196/36741.
N. Khera, N. Zhang, T. Hilal, U. Durani, M. Lee, R. Padman, S. Voleti, R.M. Warsame, B. Borah, K. R. Yabroff, J. M. Griffin. 2022. Association of Health Insurance Literacy with Financial Hardship in Patients with Cancer. JAMA Network Open.2022;5(7):e2223141. doi:10.1001/jamanetworkopen. 2022.23141.
K. Shehadeh, R. Padman. 2022. Stochastic Optimization Approaches for Elective Surgery Scheduling and Downstream Capacity Planning: Models, Challenges, and Opportunities. Computers and Operations Research, 137 (2022) 105523.
WN Chi, C Reamer, R Gordon, N Sarswat, C Gupta, EW VanGompel, J Dayiantis, M Morton-Jost, K Larimer, DE Victorson, L Halasyamani, A Solomonides, R Padman, NS Shah. 2021. Continuous Remote Patient Monitoring: Evaluation of the Heart Failure Cascade Soft Launch. Applied Clinical Informatics, 2021;12:1161–1173.
F. Movahedi, R. Padman, J. Antaki. 2021. Limitations of receiver operating characteristic curve on imbalanced data: Assist device mortality risk scores. Journal of Thoracic and Cardiovascular Surgery, Volume 165, Issue 4, Pages 1433-1442.e2. doi.org/10.1016/j.jtcvs.2021.07.041
Kilic A, Dochtermann D, Padman R, Miller JK, Dubrawski A (2021). Using machine learning to improve risk prediction in durable left ventricular assist devices. PLOS ONE 16(3): e0247866. https://doi.org/10.1371/journal.pone.0247866.
K. Shehadeh, R. Padman (2021). A Distributionally Robust Optimization Approach for Stochastic Elective Surgery Scheduling with Limited Intensive Care Unit Capacity. European Journal of Operational Research. Volume 290, Issue 3, 2021, Pages 901-913, ISSN 0377-2217, https://doi.org/10.1016/j.ejor.2020.09.001.
Kato-Lin Y, Kumar UB, Sri Prakash B, Prakash B, Varadan V, Agnihotri S, Subramanyam N, Krishnatray P, Padman R (2020). Impact of Pediatric Mobile Game Play on Healthy Eating Behavior: Randomized Controlled Trial. JMIR Mhealth Uhealth 2020;8(11):e15717 doi: 10.2196/15717.
Kilic A, Macickova J, Duan L, Movahedi F, Seese L, Zhang Y, Jacoski MV, Padman R. Machine Learning Approaches to Analyzing Adverse Events Following Durable LVAD Implantation (2020). Annals of Thoracic Surgery (accepted, in press).
M. Chen, X. Tan, R. Padman (2020). Social Determinants of Health in Electronic Health Records and Their Impact on Analysis and Risk Prediction: A Systematic Review. Journal of the American Medical Informatics Association, Volume 27, Issue 11, pp. 1764-1773, https://doi.org/10.1093/jamia/ocaa143.
R. Kamaleswaran, J. Lian, D. Lin, H. Molakapuri, S. Nunna, P. Shah, S. Dua, R. Padman (2020). Predicting Volume Responsiveness Among Sepsis Patients Using Clinical Data and Continuous Physiological Waveforms. AMIA Annu Symp Proc. 2020.
W. Wang, H. Zhao, H. Zuang, N. Shah, R. Padman (2020). DyCRS: Dynamic Interpretable Postoperative Complication Risk Scoring. WWW’20: Proceedings of the Web Conference 2020, Taipei, Taiwan, https://doi.org/10.1145/3366423.3380253.
X. Liu, A. Susarla, B. Zhang, R. Padman (2020). Go To YouTube and Call Me in the Morning: Use of Social Media for Chronic Conditions. Special Issue of MIS Quarterly on “The Role of Information Systems and Analytics in Chronic Disease Prevention and Management”, 257-283; DOI: 10.25300/MISQ/2020/15107.
O. Ben-Assuli, R. Padman (2020). Trajectories of Repeated Readmissions of Chronic Disease Patients: Risk Stratification, Profiling, and Predictions. Special Issue of MIS Quarterly on “The Role of Information Systems and Analytics in Chronic Disease Prevention and Management”, 201-226; DOI: 10.25300/MISQ/2020/15101.
F. Movahedi, R. L. Kormos, L. Lohmueller, L. Seese, M. Kanwar, S. Murali, Y. Zhang, R. Padman, J. F. Antaki (2019). Sequential Pattern Mining of Longitudinal Adverse Events After Left Ventricular Assist Device Implant. IEEE Journal of Biomedical and Health Informatics. DOI: 10.1109/jbhi.2019.2958714.
D. Gartner, R. Padman (2019). Flexible Hospital-wide Elective Patient Scheduling. Journal of the Operational Research Society (JORS). DOI: 10.1080/01605682.2019.1590509.
Y.C Lin, R. Padman (2019). RFID Technology-Enabled Markov Reward Process for Sequencing Care Coordination in Ambulatory Care: A Case Study. International Journal of Information Management, Volume 48, Pages 12-2. https://doi.org/10.1016/j.ijinfomgt.2019.01.018.
D. Gartner, R. Padman (2019). Machine Learning for Behavioral Healthcare Analytics: Addressing Waiting Time Perceptions in Emergency Care. Journal of the Operational Research Society Special Issue on Healthcare Behavioural OR, 1-14.
L. Seese, F. Movahedi, J. Antaki, R. Padman (2019). Delineating Pathways to Death by Multisystem Organ Failure in Patients with a Left Ventricular Assist Device (LVAD). The Journal of Heart and Lung Transplantation 38(4):S354; DOI: 10.1016/j.healun.2019.01.900.
O. Ben-Assuli, R. Padman, I. Shabtai (2019). Exploring Trajectories of Frequent Emergency Department Visits using a Laboratory-based Indicator of Serious Illness. Healthcare Informatics Journal. 22:1460458218824751. doi: 10.1177/1460458218824751.
H. Hao, R. Padman, B. Sun, R. Telang (2018). Quantifying the Impact of Social Learning on Information Technology Adoption: A Hierarchical Bayesian Learning Approach. Information Systems Research, 29 (1): 25-41.