Summary of Augmented Risk Prediction For the Onset Of Alzheimer’s Disease From Electronic Health Records with Large Language Models, by Jiankun Wang et al.
Augmented Risk Prediction for the Onset of Alzheimer’s Disease from Electronic Health Records with Large Language Models
by Jiankun Wang, Sumyeong Ahn, Taykhoom Dalal, Xiaodan Zhang, Weishen Pan, Qiannan Zhang, Bin Chen, Hiroko H. Dodge, Fei Wang, Jiayu Zhou
First submitted to arxiv on: 26 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Applications (stat.AP)
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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 proposed pipeline combines traditional supervised learning methods (SLs) and large language models (LLMs) to enhance risk prediction in Alzheimer’s disease and related dementias. The approach leverages the strengths of SLs in clear-cut cases and LLMs in more complex scenarios, using a confidence-driven decision-making mechanism. This novel combination demonstrates significant improvements in predictive performance when evaluated using a real-world electronic health record (EHR) data warehouse from Oregon Health & Science University Hospital. The potential implications for revolutionizing ADRD screening and early detection practices are promising, with the potential to improve patient management and healthcare strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict Alzheimer’s disease is being developed by combining two types of computer learning models. One type, called supervised learning methods, is good at making predictions when there is a lot of information available. The other type, large language models, can make predictions even when there isn’t much information. By working together, these models can improve the accuracy of Alzheimer’s disease predictions. This new approach has been tested using data from over 2.5 million patients and shows great promise for helping doctors diagnose Alzheimer’s disease earlier and more accurately. |
Keywords
» Artificial intelligence » Supervised
