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Additional file 1 of Impact of the Covid-19 pandemic on the performance of machine learning algorithms for predicting perioperative mortality

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FormulationGPT Is Not a Formulator: Governing Generative Artificial Intelligence across Excipient Selection, Process Design, and Product Decisions

Thompson, D., Mitchell, D. S., Adams, R., Brown, J., & Lee, M. (2026) · International Journal Of Pharmaceutical Research And Allied Sciences · doi:10.51847/p5svbmxcbk

Safety after the Average Patient: Detecting Heterogeneous Drug Harm across Age, Sex, Comorbidity, Genetics, and Concomitant Treatment

Dupuis, C., Perrin, H., Morel, E., & Martin, T. (2025) · International Journal Of Pharmaceutical Research And Allied Sciences · doi:10.51847/2vx9iuavw1

From Statistical Signal Detection to Causal Understanding of Drug Harm through Traceable, Multisource Pharmacovigilance Evidence for Decision-Making

Hassan, L., Khalaf, O., Jaber, R., & Mostafa, R. H. (2025) · International Journal Of Pharmaceutical Research And Allied Sciences · doi:10.51847/5fa9e0jz4r

Adaptive Pharmacovigilance under Model Drift, Product Evolution, Data-Source Change, and Emerging Patterns of Medicine Use across Global Health Systems

Larsson, S., Johansson, E., Nilsson, A., & Andersson, L. (2026) · International Journal Of Pharmaceutical Research And Allied Sciences · doi:10.51847/tqergeaspd

Temporal maintenance of an adolescent suicide attempt screening score in national surveys

Xiaotian, M. A., Su, J., & Zhang, J. (2026) · Medicine · doi:10.1097/md.0000000000050513

Development and External Validation of a Treatment-Adjusted Machine Learning Model to Support Risk-Informed Group-Based Depression Care for People Living with HIV in Uganda

Babirye, S., Huang, R., Zeng, C., Kwesiga, J. M., Kemigisha, B., Wamala, K., Kasujja, R., Kalani, K. O., Andres, R., & Nakimuli‐Mpungu, E. (2026) · Wellcome Open Research · doi:10.12688/wellcomeopenres.25768.2

Dynamic survival prediction using longitudinal AGR in unresectable locally advanced esophageal squamous cell carcinoma

Du, F., Deng, W., Xiao, Y., Yu, R., & Jia, J. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1909638

Predicting in-hospital mortality after cardiac surgery using machine learning: temporal validation in the MIMIC-IV database

Zhou, J., & Zhang, C. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1914633

Post-Deployment Monitoring of Clinical AI Systems: Drift Detection, Feedback Governance, and Update Policies

Nkosi, T., & Molefe, L. (2024) · Journal of Health Informatics and Digital Systems · doi:10.68159/k913322666

Calibration Drift Among Regression and Machine Learning Models for Hospital Mortality.

Davis, S. E., Lasko, T. A., Chen, G., & Matheny, M. E. (2017) · PubMed Central · Cited by 50

Analyzing and Mitigating Model Drift in Acute Kidney Injury Prediction for Hospitalized Patients.

Xu, Z., Li, D., Xu, Q., Chan, H. Y., Asl, Y., & Liu, M. (2024) · PubMed

Strategies to Mitigate Model Drift of a Machine Learning Prediction Model for Acute Kidney Injury in General Hospitalization

Xu, J., Liu, H., Li, G., Mi, W., Gallagher, M., & Feng, Y. (2025) · International Journal of Intelligent Systems · doi:10.1155/int/2240862

Detection of calibration drift in clinical prediction models to inform model updating

Davis, S. E., Greevy, R. A., Lasko, T. A., Walsh, C. G., & Matheny, M. E. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103611 · Cited by 160

A novel lifelong machine learning-based method to eliminate calibration drift in clinical prediction models

Chi, S., Tian, Y., Wang, F., Zhou, T., Jin, S., & Li, J. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102256 · Cited by 46