Summary of Understanding Egfr Trajectories and Kidney Function Decline Via Large Multimodal Models, by Chih-yuan Li et al.
Understanding eGFR Trajectories and Kidney Function Decline via Large Multimodal Models
by Chih-Yuan Li, Jun-Ting Wu, Chan Hsu, Ming-Yen Lin, Yihuang Kang
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 abstract presents a study on predicting future estimated Glomerular Filtration Rate (eGFR) levels using Large Multimodal Models (LMMs), which are robust foundation models. The study uses a dataset of laboratory and clinical values from 50 patients and investigates the potential of LMMs for predicting eGFR levels by integrating various prompting techniques and ensembles of LMMs. The findings suggest that these models, when combined with precise prompts and visual representations of eGFR trajectories, offer predictive performance comparable to existing Machine Learning (ML) models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is trying to find a better way to predict how well kidneys are working in the future. They’re using special kinds of computer models called Large Multimodal Models (LMMs). These models can look at lots of different types of information, like lab tests and patient records. The researchers used data from 50 patients to see if they could use LMMs to predict how well their kidneys would be working in the future. They found that by using these models with special prompts and pictures of kidney function over time, they could make predictions that are just as good as other computer models. This is an important step forward for doctors who want to help patients with kidney problems. |
Keywords
» Artificial intelligence » Machine learning » Prompting
