Integrating NLP to Enhance Algorithmic Identification of Metastatic and Castration-Resistant Prostate Cancer in Large Claims-Based Studies.

ABSTRACT:

Purpose

Accurate classification of prostate cancer (PC) disease states defined by the presence or absence of metastasis and castration resistance (CRPC) is critical but challenging in population-based research. As chart review is not feasible on a large scale, accurate automated methods are needed.

Methods

We conducted a retrospective study using data from the Veterans Affairs Health Care System to evaluate algorithms for identifying CRPC and metastatic PC, with manual chart review as the gold standard. Our analysis included 8336 patients for CRPC classification and 721 for metastatic disease classification. For CRPC classification, we assessed one novel algorithm using criteria including rising prostate-specific antigen levels or progression to metastatic disease while rece

SUBMITTER: Stock SR 

PROVIDER: S-EPMC12719047 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature