Summary of Synsum — Synthetic Benchmark with Structured and Unstructured Medical Records, by Paloma Rabaey et al.
SynSUM – Synthetic Benchmark with Structured and Unstructured Medical Records
by Paloma Rabaey, Henri Arno, Stefan Heytens, Thomas Demeester
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces the SynSUM benchmark, a large-scale synthetic dataset linking unstructured clinical notes to structured background variables. The dataset comprises 10,000 artificial patient records with both tabular variables and related clinical notes describing respiratory disease encounters. A Bayesian network is used to generate tabular data based on domain knowledge, while a language model (GPT-4o) generates clinical notes from the tabular data. Expert evaluation assesses the quality of generated notes, and simple predictor models are run on both datasets, providing a baseline for further research. The SynSUM dataset facilitates studies on clinical information extraction, automating clinical reasoning, causal effect estimation, and multi-modal synthetic data generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special kind of computer data called SynSUM. It’s like a big book with many fake patient stories and related numbers (like symptoms and diagnoses). A special math formula helps create these numbers based on what doctors know about respiratory diseases. Then, a computer program writes short notes about each patient’s story. Experts look at the generated notes to see how good they are, and some simple calculations are done using both the numbers and text data. This dataset is useful for studying how computers can help doctors make sense of patient information. |
Keywords
» Artificial intelligence » Bayesian network » Gpt » Language model » Multi modal » Synthetic data
