Summary of Covidllm: a Robust Large Language Model with Missing Value Adaptation and Multi-objective Learning Strategy For Predicting Disease Severity and Clinical Outcomes in Covid-19 Patients, by Shengjun Zhu (1) et al.
CovidLLM: A Robust Large Language Model with Missing Value Adaptation and Multi-Objective Learning Strategy for Predicting Disease Severity and Clinical Outcomes in COVID-19 Patients
by Shengjun Zhu, Siyu Liu, Yang Li, Qing Lei, Hongyan Hou, Hewei Jiang, Shujuan Guo, Feng Wang, Rongshang Chen, Xionglin Fan, Shengce Tao, Jiaxin Cai
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 investigates the application of large language models (LLMs) for early identification of severe COVID-19 outcomes in high-risk patients. Traditional machine learning and deep learning models have been used to predict disease severity and clinical outcomes, but LLMs offer a unique approach by leveraging semantic understanding and explicit handling of missing data. The authors construct specialized prompts and adopt multi-objective learning strategies, using serological indicators as input data. They fine-tune the ChatGLM model for this task and demonstrate its effectiveness in predicting disease severity and clinical outcomes. This work has promising potential for further development and may improve patient care by enabling early identification of high-risk patients. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how to use special computer models, called large language models, to predict the severity of COVID-19 in people who are most at risk. These models can analyze blood test samples and understand missing data without filling it in first. The researchers created specific questions for the model to answer and trained it on data that shows how well it does. They tested this approach using a certain type of model called ChatGLM, and it worked really well! This could help doctors identify people who need extra care sooner, which might save lives. |
Keywords
» Artificial intelligence » Deep learning » Machine learning
