1 citations · 3 across the 9 of their papers we have counts for
11 papers
Towards Error-Free EHRs: Reasoning-Intensive Consistency Verification Between Clinical Notes and Structured Tables in Electronic Health Records
Yeonsu Kwon, Jiho Kim, Junseong Choi +10
Data consistency between unstructured clinical notes and structured tables in Electronic Health Records (EHRs) is essential for patient safety and clinical decision-making. However…
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…
Modeling Clinical Uncertainty in Radiology Reports: from Explicit Uncertainty Markers to Implicit Reasoning Pathways
Paloma Rabaey, Jong Hak Moon, Jung-Oh Lee +4
Radiology reports are invaluable for clinical decision-making and hold great potential for automated analysis when structured into machine-readable formats. These reports often con…
Lunguage: A Benchmark for Structured and Sequential Chest X-ray Interpretation
Jong Hak Moon, Geon Choi, Paloma Rabaey +10
Radiology reports convey detailed clinical observations and capture diagnostic reasoning that evolves over time. However, existing evaluation methods are limited to single-report s…
Prior Knowledge Injection into Deep Learning Models Predicting Gene Expression from Whole Slide Images
Max Hallemeesch, Marija Pizurica, Paloma Rabaey +3
Cancer diagnosis and prognosis primarily depend on clinical parameters such as age and tumor grade, and are increasingly complemented by molecular data, such as gene expression, fr…
Debiasing Synthetic Data Generated by Deep Generative Models
Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey +4
While synthetic data hold great promise for privacy protection, their statistical analysis poses significant challenges that necessitate innovative solutions. The use of deep gener…
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…
The Real Deal Behind the Artificial Appeal: Inferential Utility of Tabular Synthetic Data
Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey +4
Recent advances in generative models facilitate the creation of synthetic data to be made available for research in privacy-sensitive contexts. However, the analysis of synthetic d…
Neural Bayesian Network Understudy
Paloma Rabaey, Cedric De Boom, Thomas Demeester
Bayesian Networks may be appealing for clinical decision-making due to their inclusion of causal knowledge, but their practical adoption remains limited as a result of their inabil…