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cs.AI2025
Patient-level Information Extraction by Consistent Integration of Textual and Tabular Evidence with Bayesian Networks
Paloma Rabaey, Adrick Tench, Stefan Heytens +1
Electronic health records (EHRs) form an invaluable resource for training clinical decision support systems. To leverage the potential of such systems in high-risk applications, we…
cs.AI2024★ 1 cited
SimSUM: Simulated Benchmark with Structured and Unstructured Medical Records
Paloma Rabaey, Stefan Heytens, Thomas Demeester
Clinical information extraction, which involves structuring clinical concepts from unstructured medical text, remains a challenging problem that could benefit from the inclusion of…
cs.AI2024
Clinical Reasoning over Tabular Data and Text with Bayesian Networks
Paloma Rabaey, Johannes Deleu, Stefan Heytens +1
Bayesian networks are well-suited for clinical reasoning on tabular data, but are less compatible with natural language data, for which neural networks provide a successful framewo…