Supervised machine learning compared to large language models for identifying functional seizures from medical records.

ABSTRACT:

Objective

The Functional Seizures Likelihood Score (FSLS) is a supervised machine learning-based diagnostic score that was developed to differentiate functional seizures (FS) from epileptic seizures (ES). In contrast to this targeted approach, large language models (LLMs) can identify patterns in data for which they were not specifically trained. To evaluate the relative benefits of each approach, we compared the diagnostic performance of the FSLS to two LLMs: ChatGPT and GPT-4.

Methods

In total, 114 anonymized cases were constructed based on patients with documented FS, ES, mixed ES and FS, or physiologic seizure-like events (PSLEs). Text-based data were presented in three sequential prompts to the LLMs, showing the history of present illness (HPI), electroencephalography (

SUBMITTER: Kerr WT 

PROVIDER: S-EPMC11997926 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature