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Practical guide to building machine learning-based clinical prediction models using imbalanced datasets.
Practical guide to building machine learning-based clinical prediction models using imbalanced datasets. Trauma surgery & acute care open Luu, J., Borisenko, E., Przekop, V., Patil, A., Forrester, J. D., Choi, J. 2024; 9 (1): e001222Abstract
Clinical prediction models often aim to predict rare, high-risk events, but building such models requires robust understanding of imbalance datasets and their unique study design considerations. This practical guide highlights foundational prediction model principles for surgeon-data scientists and readers who encounter clinical prediction models, from feature engineering and algorithm selection strategies to model evaluation and design techniques specific to imbalanced datasets. We walk through a clinical example using readable code to highlight important considerations and common pitfalls in developing machine learning-based prediction models. We hope this practical guide facilitates developing and critically appraising robust clinical prediction models for the surgical community.
View details for DOI 10.1136/tsaco-2023-001222
View details for PubMedID 38881829
View details for PubMedCentralID PMC11177772