Shah, Nigam H.

Assessing the net benefit of machine learning models in the presence of resource constraints

Singh, K., Shah, N. H., & Vickers, A. J. (2023) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 21

Improving hospital readmission prediction using individualized utility analysis

Ko, M., Chen, E., Agrawal, A., Rajpurkar, P., Avati, A., Ng, A., Basu, S., & Shah, N. H. (2021) · Journal of Biomedical Informatics

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 15

Automating the Enterprise with Foundation Models

Wornow, M., Narayan, A., Opsahl-Ong, K., McIntyre, Q., Shah, N. H., & Re, C. (2024) · arXiv

Robotic Process Automation ApplicationsBusiness Process Modeling and Analysis

Cited by 15

Automating the Enterprise with Foundation Models

Wornow, M., Narayan, A., Opsahl-Ong, K., McIntyre, Q., Shah, N., & Ré, C. (2024) · Proceedings of the VLDB Endowment

Robotic Process Automation ApplicationsBusiness Process Modeling and Analysis

Cited by 15

Why and How to Monitor Deployed AI Systems in Health Care

Keyes, T., Callahan, A., Pandya, A. S., Ambers, N., Banda, J. M., Fuentes, M., Lugtu, C., Masariya, P., Nallan, S., O’Brien, C., et al. (2026) · NEJM Catalyst

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 10

Comment on: “Zoo or Savannah? Choice of Training Ground for Evidence-Based Pharmacovigilance”

Harpaz, R., DuMouchel, W., & Shah, N. H. (2015) · Drug Safety

Pharmacovigilance and Adverse Drug ReactionsData-Driven Disease Surveillance

Cited by 9

Monitoring Deployed AI Systems in Health Care

Keyes, T., Callahan, A., Pandya, A. S., Ambers, N., Banda, J. M., Fuentes, M., Lugtu, C., Masariya, P., Nallan, S., O'Brien, C., et al. (2025) · arXiv

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 3