Interpretable machine learning-based individual analysis of acute kidney injury in immune checkpoint inhibitor therapy.

ABSTRACT:

Background

Acute kidney injury (AKI) is a critical complication of immune checkpoint inhibitor therapy. Since the etiology of AKI in patients undergoing cancer therapy varies, clarifying underlying causes in individual cases is critical for optimal cancer treatment. Although it is essential to individually analyze immune checkpoint inhibitor-treated patients for underlying pathologies for each AKI episode, these analyses have not been realized. Herein, we aimed to individually clarify the underlying causes of AKI in immune checkpoint inhibitor-treated patients using a new clustering approach with Shapley Additive exPlanations (SHAP).

Methods

We developed a gradient-boosting decision tree-based machine learning model continuously predicting AKI within 7 days, using the medica

SUBMITTER: Sakuragi M 

PROVIDER: S-EPMC10950216 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature