1 citations · 3 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
From Text to Treatment Effects: A Meta-Learning Approach to Handling Text-Based Confounding
Henri Arno, Paloma Rabaey, Thomas Demeester
One of the central goals of causal machine learning is the accurate estimation of heterogeneous treatment effects from observational data. In recent years, meta-learning has emerge…
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…
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…