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ML Application · Python

masld-fib4

Who FIB-4 misses in MASLD: phenotyping fibrosis false negatives on NHANES 2017-2020. Oral at APASL STC 2026, Kumamoto.

What it is

The repository presents a machine learning study to characterize FIB-4 false negatives in MASLD using NHANES data, employing XGBoost, SHAP, clustering, and LLMs to identify phenotypes and develop an improved triage score (FIB-4 Plus).

  • clinical-ml
  • fibrosis
  • masld
  • nhanes
  • shap
  • xgboost
Scored atf8889b19 Nov 2026Changes since

Merit describes the code at this commit. Later pushes count once the owner rescans. How merit is scored

Where it counts

  • Machine Learning90%
  • Data Science80%
  • NLP & LLMs70%
  • MLOps20%

How much of the project is about each field, as scoring counts it. It ranks in the field it is most about.

What the judge found

Read from the project's code at its scored commit.

Strengths4

  • Comprehensive pipeline integrating multiple ML techniques for a clinical problem.
  • Clear documentation and reproducibility instructions (Quickstart).
  • Novel application of LLMs for phenotyping with validation against SHAP.
  • Clinically relevant findings presented at a conference.

Limitations

The owner keeps this project's limitations private.

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