Early Prediction of Bronchopulmonary Dysplasia Using Clinical Data From the First Three Postnatal Weeks in Preterm Infants: A Retrospective Study With Large Language Models

Premature birth remains a major risk factor for neonatal morbidity and mortality, with bronchopulmonary dysplasia (BPD) representing one of the most significant chronic pulmonary complications in very preterm infants. Despite advances in neonatal intensive care, early and accurate prediction of BPD remains challenging due to the multifactorial nature of its pathophysiology, involving respiratory support requirements, oxygen exposure, infection burden, and perinatal factors.

This retrospective study evaluates the feasibility of using large language models (LLMs) for early prediction of BPD based on structured clinical data extracted from neonatal intensive care unit (NICU) records. Clinical variables are organized into weekly datasets corresponding to the first, second, and third postnatal weeks to capture the dynamic evolution of respiratory status and clinical condition over time.

Standardized and anonymized patient-level datasets are formatted into structured prompts and provided to multiple LLMs (ChatGPT, Gemini, and Claude). Each model receives identical input variables to ensure comparability. The models are instructed to generate categorical risk stratification (low, medium, high) along with corresponding probability estimates for BPD development.

To ensure methodological consistency, prompt engineering is standardized across all models and time points. Outputs are recorded for each weekly time window, allowing temporal comparison of predictive performance and assessment of how early postnatal data influences model accuracy.

Model outputs are subsequently compared with confirmed clinical outcomes of BPD development in the study population. Performance evaluation focuses on discriminative ability and calibration of predictions across different time points and models.