Can I read SynSUM -- Synthetic Benchmark with Structured and Unstructured Medical Records on EtoBox?

SynSUM -- Synthetic Benchmark with Structured and Unstructured Medical Records by Rabaey, Paloma; Arno, Henri; Heytens, Stefan; Demeester, Thomas is a scholarly article available to read on EtoBox.

What is SynSUM -- Synthetic Benchmark with Structured and Unstructured Medical Records about?

We present the SynSUM benchmark, a synthetic dataset linking unstructured clinical notes to structured background variables. The dataset consists of 10,000 artificial patient records containing tabular variables (like symptoms, diagnoses and underlying conditions) and related notes describing the fictional patient encounter in the domain of respiratory diseases. The tabular portion of the data is generated through a Bayesian network, where both the causal structure between the variables and the conditional probabilities are proposed by an expert based on domain knowledge. We then prompt a large language model (GPT-4o) to generate a clinical note related to this patient encounter, describing the patient symptoms and additional context. We conduct both an expert evaluation study to assess the quality of the generated notes, as well as running some simple predictor models on both the tabular and text portions of the dataset, forming a baseline for further research. The SynSUM dataset is primarily designed to facilitate research on clinical information extraction in the presence of tabular background variables, which can be linked through domain knowledge to concepts of interest to be

Author

Rabaey, Paloma; Arno, Henri; Heytens, Stefan; Demeester, Thomas

Published

2024

Language

EN