Summary of Improving Mortality Prediction After Radiotherapy with Large Language Model Structuring Of Large-scale Unstructured Electronic Health Records, by Sangjoon Park et al.
Improving Mortality Prediction After Radiotherapy with Large Language Model Structuring of Large-Scale Unstructured Electronic Health Records
by Sangjoon Park, Chan Woo Wee, Seo Hee Choi, Kyung Hwan Kim, Jee Suk Chang, Hong In Yoon, Ik Jae Lee, Yong Bae Kim, Jaeho Cho, Ki Chang Keum, Chang Geol Lee, Hwa Kyung Byun, Woong Sub Koom
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The RT-Surv framework integrates large language models with electronic health records and structured clinical data to predict survival rates in radiotherapy patients. By using open-source models to structure unstructured clinical information, the framework achieved a significant improvement in concordance index during external validation, demonstrating its potential to transform unstructured data into actionable insights. The study found that key features like disease extent, general condition, and RT purpose showed high predictive importance and aligned with statistically significant predictors identified through conventional statistical analyses. These findings have implications for improving predictive modeling and patient outcomes in clinics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper developed a new way to predict how well people will survive after getting radiotherapy treatment. They used big language models that are usually used for other things, like understanding what’s written on the internet, but they modified them to help doctors make better decisions. The researchers took lots of medical records and used these language models to turn unorganized information into organized data that computers can understand. This helped doctors predict how well people will survive much better than before. It also showed that some important factors like how bad the disease is and what the doctor is trying to achieve with treatment were very important in making those predictions. |
