Harnessing Large Language Models in Neonatal Intraventricular Hemorrhage: Exploring Retrieval Augmented Generation Methodology for Prognostic Variable Discovery
Abstract
OBJECTIVE: The objective of this study is to evaluate whether large language models (LLMs) can autonomously synthesize existing literature and accurately extract prognostic variables for neonatal intraventricular hemorrhage (IVH) and its outcomes while assessing their capability for clinical feature ranking and risk stratification.
STUDY DESIGN: This pilot study employed a systematic literature review combined with retrieval-augmented generation (RAG) methodology. GPT 4 (OpenAI) and Claude Sonnet (4.0, Anthropic) were prompted to identify peer-reviewed studies utilizing machine learning and deep learning to predict IVH outcomes in preterm neonates. Data extraction was prompted to follow TRIPOD artificial intelligence (AI) guidelines, capturing study design, population characteristics, predictor variables, and outcome measures. Semi-automated RAG extraction was performed with manual validation to mitigate hallucination risk.
RESULTS: LLMs initially identified 39 studies, with 28 meeting some or all the validation criteria after excluding references that were hallucinated. From these, 14 distinct prognostic predictors were extracted across four outcome domains: mortality, progression, complications, and resolution. Universal high-impact predictors included gestational age (13 mentions; 41%), birth weight (8 mentions, 25%), and Apgar scores (11 mentions, 34%). Variables were categorized into three clinical tiers based on frequency, outcome breadth, and modifiability. A…
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