Common laboratory results-based artificial intelligence analysis achieves accurate classification of plasma cell dyscrasias.

ABSTRACT:

Background

Plasma cell dyscrasias encompass a diverse set of disorders, where early and precise diagnosis is essential for optimizing patient outcomes. Despite advancements, current diagnostic methodologies remain underutilized in applying artificial intelligence (AI) to routine laboratory data. This study seeks to construct an AI-driven model leveraging standard laboratory parameters to enhance diagnostic accuracy and classification efficiency in plasma cell dyscrasias.

Methods

Data from 1,188 participants (609 with plasma cell dyscrasias and 579 controls) collected between 2018 and 2023 were analyzed. Initial variable selection employed Kruskal-Wallis and Wilcoxon tests, followed by dimensionality reduction and variable prioritization using the Shapley Additive Explanation

SUBMITTER: Yao B 

PROVIDER: S-EPMC11542560 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature