LCD benchmark: long clinical document benchmark on mortality prediction for language models.

ABSTRACT:

Objectives

The application of natural language processing (NLP) in the clinical domain is important due to the rich unstructured information in clinical documents, which often remains inaccessible in structured data. When applying NLP methods to a certain domain, the role of benchmark datasets is crucial as benchmark datasets not only guide the selection of best-performing models but also enable the assessment of the reliability of the generated outputs. Despite the recent availability of language models capable of longer context, benchmark datasets targeting long clinical document classification tasks are absent.

Materials and methods

To address this issue, we propose Long Clinical Document (LCD) benchmark, a benchmark for the task of predicting 30-day out-of-hospital morta

SUBMITTER: Yoon W 

PROVIDER: S-EPMC11756648 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature