Precision Medicine - 2018

Shi, C., Song, R., & Lu, W. (2018). Discussion of ’Optimal treatment allocations in space and time for on-line control of an emerging infectious disease’ [Review of Optimal treatment allocations in space and time for on-line control of an emerging infectious disease, by E. Laber, N. Meyer, B. Reich, K. Pacifici, J. Collazo, & J. Drake]. Journal of the Royal Statistical Society, Series C, 67(4), 775–776.

Özaltin, O. Y., Prokopyev, O. A., & Schaefer, A. J. (2018). Optimal design of the seasonal influenza vaccine with manufacturing autonomy. INFORMS Journal on Computing, 30(2), 371–387. https://doi.org/10.1287/ijoc.2017.0786

Zare, M. H., Özalt?n, O. Y., & Prokopyev, O. A. (2018). On a class of bilevel linear mixed-integer programs in adversarial settings. Journal of Global Optimization, 71(1), 91–113. https://doi.org/10.1007/s10898-017-0549-2

Laber, E. B., Rose, E. J., Davidian, M., & Tsiatis, A. A. (2018). Q-Learning. https://doi.org/10.1002/9781118445112.stat07998

Luo, S., Song, R., Styner, M., Gilmore, J. H., & Zhu, H. (2018). FSEM: Functional Structural Equation Models for Twin Functional Data. Journal of the American Statistical Association, 114(525), 344–357. https://doi.org/10.1080/01621459.2017.1407773

Zhu, W., Zeng, D., & Song, R. (2018). Proper Inference for Value Function in High-Dimensional Q-Learning for Dynamic Treatment Regimes. Journal of the American Statistical Association, 114(527), 1404–1417. https://doi.org/10.1080/01621459.2018.1506341

Liang, S., Lu, W., & Song, R. (2018). Deep advantage learning for optimal dynamic treatment regime. Statistical Theory and Related Fields, 2(1), 80–88. https://doi.org/10.1080/24754269.2018.1466096

Nasrollahzadeh, A. A., Khademi, A., & Mayorga, M. E. (2018). Real-Time Ambulance Dispatching and Relocation. Manufacturing & Service Operations Management, 20(3), 467–480. https://doi.org/10.1287/msom.2017.0649

Rutter, E. M., Lagergren, J. H., & Flores, K. B. (2018). Automated Object Tracing for Biomedical Image Segmentation Using a Deep Convolutional Neural Network. MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT IV, Vol. 11073, pp. 686–694. https://doi.org/10.1007/978-3-030-00937-3_78

Rutter, E. M., Langdale, C. L., Hokanson, J. A., Hamilton, F., Tran, H., Grill, W. M., & Flores, K. B. (2018). Detection of Bladder Contractions From the Activity of the External Urethral Sphincter in Rats Using Sparse Regression. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(8), 1636–1644. https://doi.org/10.1109/tnsre.2018.2854675

Hager, R., Tsiatis, A. A., & Davidian, M. (2018). Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data. BIOMETRICS, 74(4), 1180–1192. https://doi.org/10.1111/biom.12894

Zhang, Y., Laber, E. B., Davidian, M., & Tsiatis, A. A. (2018). Interpretable Dynamic Treatment Regimes. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 113(524), 1541–1549. https://doi.org/10.1080/01621459.2017.1345743

Shi, C., Lu, W., & Song, R. (2018). A Massive Data Framework for M-Estimators with Cubic-Rate. Journal of the American Statistical Association, 113(524), 1698–1709. https://doi.org/10.1080/01621459.2017.1360779

Rutter, E. M., Banks, H. T., & Flores, K. B. (2018). Estimating intratumoral heterogeneity from spatiotemporal data. JOURNAL OF MATHEMATICAL BIOLOGY, 77(6-7), 1999–2022. https://doi.org/10.1007/s00285-018-1238-6

Wang, L., Zhou, Y., Song, R., & Sherwood, B. (2018). Quantile-Optimal Treatment Regimes. Journal of the American Statistical Association, 113(523), 1243–1254. https://doi.org/10.1080/01621459.2017.1330204

Enayati, S., Mayorga, M. E., Rajagopalan, H. K., & Saydam, C. (2018). Real-time ambulance redeployment approach to improve service coverage with fair and restricted workload for EMS providers. Omega, 79, 67–80. https://doi.org/10.1016/j.omega.2017.08.001

Shi, C., Song, R., Lu, W., & Fu, B. (2018). Maximin projection learning for optimal treatment decision with heterogeneous individualized treatment effects. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 80(4), 681–702. https://doi.org/10.1111/rssb.12273

Maillart, L. M., & Mayorga, M. E. (2018, September). Introduction to the Special Issue on Advancing Health Services. SERVICE SCIENCE, Vol. 10, pp. V-VII. https://doi.org/10.1287/serv.2018.0225

Jansen, M. C., & Ozaltin, O. Y. (2018). Optimal production in a competitive market under yield uncertainty. OPTIMIZATION LETTERS, 12(7), 1487–1502. https://doi.org/10.1007/s11590-018-1288-0

Lagergren, J., Reeder, A., Hamilton, F., Smith, R. C., & Flores, K. B. (2018). Forecasting and Uncertainty Quantification Using a Hybrid of Mechanistic and Non-mechanistic Models for an Age-Structured Population Model. BULLETIN OF MATHEMATICAL BIOLOGY, 80(6), 1578–1595. https://doi.org/10.1007/s11538-018-0421-7

Enayati, S., Ozaltin, O. Y., Mayorga, M. E., & Saydam, C. (2018). Ambulance redeployment and dispatching under uncertainty with personnel workload limitations. IISE TRANSACTIONS, 50(9), 777–788. https://doi.org/10.1080/24725854.2018.1446105

Liang, S. H., Lu, W. B., Song, R., & Wang, L. (2018). Sparse concordance-assisted learning for optimal treatment decision. Journal of Machine Learning Research, 18.

Shi, C., Fan, A., Song, R., & Lu, W. (2018). HIGH-DIMENSIONAL A-LEARNING FOR OPTIMAL DYNAMIC TREATMENT REGIMES. ANNALS OF STATISTICS, 46(3), 925–957. https://doi.org/10.1214/17-aos1570

Agor, J. K., & Ozaltin, O. Y. (2018). [Review of Models for predicting the evolution of influenza to inform vaccine strain selection]. HUMAN VACCINES & IMMUNOTHERAPEUTICS, 14(3), 678–683. https://doi.org/10.1080/21645515.2017.1423152