Algorithms for Healthcare

  1. Hotspots for Emerging Epidemics: Multi-Task and Transfer Learning over Mobility Networks
  2. Control Of Epidemic Spreads Via Testing And Lock-Down
  3. Hsu, W., Hippe, D.S., Nakhaei, N., Wang, P.C., Zhu, B., Siu, N., Ahsen, M.E., Lotter, W., Sorensen, A.G., Naeim, A. and Buist, D.S., 2022. External Validation of an Ensemble Model for Automated Mammography Interpretation by Artificial Intelligence. JAMA network open, 5(11), pp.e2242343-e2242343.
  4. Manica, M., Bunne, C., Mathis, R., Cadow, J., Ahsen, M.E., Stolovitzky, G.A. and Martínez, M.R., 2021. COSIFER: a Python package for the consensus inference of molecular interaction networks. Bioinformatics, 37(14), pp.2070-2072.
  5. Roy, S., Kiral, I., Mirmomeni, M., Mummert, T., Braz, A., Tsay, J., Tang, J., Asif, U., Schaffter, T., Ahsen, M.E. and Iwamori, T., 2021. Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data. EBioMedicine, 66, p.103275.
  6. Schaffter, T., Buist, D.S., Lee, C.I., Ahsen, M.E., Nikulin, Y., Ribli, D., Guan, Y., Lotter, W., Jie, Z., Du, H., Wang, S. and Feng, J., 2020. Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms. JAMA network open, 3(3), pp.e200265-e200265.
  7. Ayvaci, M.U.S., Alagoz, O., Ahsen, M.E. and Burnside, E.S., 2018. Preference‐Sensitive Management of Post‐Mammography Decisions in Breast Cancer Diagnosis. Production and operations management, 27(12), pp.2313-2338.
  8. Ayvaci, M.U.S., Ahsen, M.E., Raghunathan, S. and Gharibi, Z., 2017. Timing the Use of Breast Cancer Risk Information in Biopsy Decision‐Making. Production and Operations Management, 26(7), pp.1333-1358.

Algorithmic Bias/Fairness in Healthcare

  1. Ahsen, M. E., Ayvaci, M. U. S., & Raghunathan, S. (2019). When algorithmic predictions use human-generated data: A bias-aware classification algorithm for breast cancer diagnosis. Information Systems Research, 30(1), 97-116.

  2. Zhang, Z., Genc, Y., Wang, D., Ahsen, M.E. and Fan, X., 2021. Effect of ai explanations on human perceptions of patient-facing ai-powered healthcare systems. Journal of Medical Systems, 45(6), pp.1-10.

Healthcare Operations Management

  • Evaluation of reopening strategies for educational institutions during COVID-19 through agent based simulation
  • Not All COVID-19 Waves are Similar: Origins, Detection and Mitigation Strategies for Simultaneous Waves
  • How SHIELD Illinois can help keep educational institutions open amid COVID-19: An econometric and epidemiologic analysis of ‘Test-to-stay’ program
  • Jiang, Z. Z., Silberholz, J., Wang, Y. I., Costa, D., & Sjoding, M. (2022). Can Employees’ Past Helping Behavior Be Used to Improve Shift Scheduling? Evidence from ICU Nurses. Evidence from ICU Nurses (February 14, 2022).
  • Xueze Song, Mili Mehrotra, Tharanga Rajapakshe, “An Analysis of Incentive Schemes for Participant Retention in Clinical Studies,” MSOM, Article in Advance, 2022, Available at https://doi.org/10.1287/msom.2022.1184.
  • Diwakar Gupta, Mili Mehrotra, Xiaoxu Tang, “Gainsharing Contracts for CMS’ Episode- Based Payment Models,” POM, Vol. 30, No. 5, May 2021, pp. 290-1312.
  • Mili Mehrotra, Karthik Natarajan, “Value of Combining Patient and Provider Incentives in Humanitarian Healthcare Service Programs,” POM, Vol. 29, No. 3, March 2020, pp. 571-594.
  • Diwakar Gupta, Mili Mehrotra, “Bundling Payments for Healthcare Services: Proposer Selection and Information Sharing,” Operations Research, Vol. 63, No. 4, July 2015, pp. 772-788.

Machine Learning

  1. Kim, S.C., Arun, A.S., Ahsen, M.E., Vogel, R. and Stolovitzky, G., 2021. The Fermi–Dirac distribution provides a calibrated probabilistic output for binary classifiers. Proceedings of the National Academy of Sciences, 118(34), p.e2100761118.
  2. Ahsen, M.E., Vogel, R.M. and Stolovitzky, G.A., 2019. Unsupervised Evaluation and Weighted Aggregation of Ranked Classification Predictions. J. Mach. Learn. Res., 20, pp.166-1.
  3. Ahsen, M.E., Challapalli, N. and Vidyasagar, M., 2017. Two new approaches to compressed sensingexhibiting both robust sparse recovery and the grouping effect. The Journal of Machine Learning Research, 18(1), pp.1745-1768.