Can GPT-3.5 generate and code discharge summaries?

ABSTRACT:

Objectives

The aim of this study was to investigate GPT-3.5 in generating and coding medical documents with International Classification of Diseases (ICD)-10 codes for data augmentation on low-resource labels.

Materials and methods

Employing GPT-3.5 we generated and coded 9606 discharge summaries based on lists of ICD-10 code descriptions of patients with infrequent (or generation) codes within the MIMIC-IV dataset. Combined with the baseline training set, this formed an augmented training set. Neural coding models were trained on baseline and augmented data and evaluated on an MIMIC-IV test set. We report micro- and macro-F1 scores on the full codeset, generation codes, and their families. Weak Hierarchical Confusion Matrices determined within-family and outside-of-family c

SUBMITTER: Falis M 

PROVIDER: S-EPMC11413433 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature