medRxiv : the preprint server for health sciences · 2025
Integrating a host transcriptomic biomarker with a large language model for diagnosis of lower respiratory tract infection.
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Van Phan Hoang, Spottiswoode Natasha, Lydon Emily C, Chu Victoria T, Cuesta Adolfo, Kazberouk Alexander D, Richmond Natalie L, Deosthale Padmini, Calfee Carolyn S, Langelier Charles R
Аннотация
BACKGROUND: Lower respiratory tract infections (LRTIs) are a leading cause of mortality worldwide and can be difficult to diagnose in critically ill patients, as non-infectious causes of respiratory failure can present with similar clinical features.
METHODS: We developed a LRTI diagnostic method combining the pulmonary transcriptomic biomarker FABP4 with electronic medical record (EMR) text assessment using the large language model Generative Pre-trained Transformer 4 (GPT-4). We evaluated this approach in a prospective cohort of critically ill adults with acute respiratory failure from whom tracheal aspirate FABP4 expression was measured by RNA sequencing. Patients with LRTI or non-infectious conditions were identified using retrospective, multi-physician clinical adjudication. We then confirmed our findings by applying this method to an independent validation cohort of 115 adults with acute respiratory failure.
RESULTS: In the derivation cohort, a combined classifier incorporating FABP4 expression and GPT-4-assisted EMR analysis achieved an AUC of 0.93 (±0.08) and an accuracy of 84%, outperforming FABP4 expression alone (AUC 0.84 ± 0.11) and GPT-4-based analysis alone (AUC 0.83 ± 0.07). By comparison, the primary medical team's admission diagnosis had an accuracy of 72%. In the validation cohort, the combined classifier yielded an AUC of 0.98 (±0.04) and an accuracy of 96%.
CONCLUSIONS: Integrating a host transcriptional biomarker with EMR text analysis using a large language model may offer a promising new approach to improving the diagnosis of LRTIs in critically ill adults.
METHODS: We developed a LRTI diagnostic method combining the pulmonary transcriptomic biomarker FABP4 with electronic medical record (EMR) text assessment using the large language model Generative Pre-trained Transformer 4 (GPT-4). We evaluated this approach in a prospective cohort of critically ill adults with acute respiratory failure from whom tracheal aspirate FABP4 expression was measured by RNA sequencing. Patients with LRTI or non-infectious conditions were identified using retrospective, multi-physician clinical adjudication. We then confirmed our findings by applying this method to an independent validation cohort of 115 adults with acute respiratory failure.
RESULTS: In the derivation cohort, a combined classifier incorporating FABP4 expression and GPT-4-assisted EMR analysis achieved an AUC of 0.93 (±0.08) and an accuracy of 84%, outperforming FABP4 expression alone (AUC 0.84 ± 0.11) and GPT-4-based analysis alone (AUC 0.83 ± 0.07). By comparison, the primary medical team's admission diagnosis had an accuracy of 72%. In the validation cohort, the combined classifier yielded an AUC of 0.98 (±0.04) and an accuracy of 96%.
CONCLUSIONS: Integrating a host transcriptional biomarker with EMR text analysis using a large language model may offer a promising new approach to improving the diagnosis of LRTIs in critically ill adults.