Practical guide to building machine learning-based clinical prediction models using imbalanced datasets.
Trauma Surg Acute Care Open
; 9(1): e001222, 2024.
Article
em En
| MEDLINE
| ID: mdl-38881829
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Trauma Surg Acute Care Open
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos