3 papers
cs.CL2026
Disentangling Ambiguity from Instability in Large Language Models: A Clinical Text-to-SQL Case Study
Angelo Ziletti, Leonardo D'Ambrosi
Deploying large language models for clinical Text-to-SQL requires distinguishing two qualitatively different causes of output diversity: (i) input ambiguity that should trigger cla…
cs.CL2025
Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation
Angelo Ziletti, Leonardo D'Ambrosi
Clinical cohort definition is crucial for patient recruitment and observational studies, yet translating inclusion/exclusion criteria into SQL queries remains challenging and manua…
cs.CL2024
Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health records
Angelo Ziletti, Leonardo D'Ambrosi
Electronic health records (EHR) and claims data are rich sources of real-world data that reflect patient health status and healthcare utilization. Querying these databases to answe…