A systematic review of machine learning-based prognostic models for acute pancreatitis: Towards improving methods and reporting quality.

ABSTRACT:

Background

An accurate prognostic tool is essential to aid clinical decision-making (e.g., patient triage) and to advance personalized medicine. However, such a prognostic tool is lacking for acute pancreatitis (AP). Increasingly machine learning (ML) techniques are being used to develop high-performing prognostic models in AP. However, methodologic and reporting quality has received little attention. High-quality reporting and study methodology are critical for model validity, reproducibility, and clinical implementation. In collaboration with content experts in ML methodology, we performed a systematic review critically appraising the quality of methodology and reporting of recently published ML AP prognostic models.

Methods/findings

Using a validated search strategy, we i

SUBMITTER: Critelli B 

PROVIDER: S-EPMC11870378 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature