Summary of Large Language Models Versus Classical Machine Learning: Performance in Covid-19 Mortality Prediction Using High-dimensional Tabular Data, by Mohammadreza Ghaffarzadeh-esfahani et al.
Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data
by Mohammadreza Ghaffarzadeh-Esfahani, Mahdi Ghaffarzadeh-Esfahani, Arian Salahi-Niri, Hossein Toreyhi, Zahra Atf, Amirali Mohsenzadeh-Kermani, Mahshad Sarikhani, Zohreh Tajabadi, Fatemeh Shojaeian, Mohammad Hassan Bagheri, Aydin Feyzi, Mohammadamin Tarighatpayma, Narges Gazmeh, Fateme Heydari, Hossein Afshar, Amirreza Allahgholipour, Farid Alimardani, Ameneh Salehi, Naghmeh Asadimanesh, Mohammad Amin Khalafi, Hadis Shabanipour, Ali Moradi, Sajjad Hossein Zadeh, Omid Yazdani, Romina Esbati, Moozhan Maleki, Danial Samiei Nasr, Amirali Soheili, Hossein Majlesi, Saba Shahsavan, Alireza Soheilipour, Nooshin Goudarzi, Erfan Taherifard, Hamidreza Hatamabadi, Jamil S Samaan, Thomas Savage, Ankit Sakhuja, Ali Soroush, Girish Nadkarni, Ilad Alavi Darazam, Mohamad Amin Pourhoseingholi, Seyed Amir Ahmad Safavi-Naini
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 abstract presents an evaluation of classical machine learning models (CMLs) and large language models (LLMs) in predicting COVID-19 mortality using a high-dimensional tabular dataset. The study compares the performance of these models, which are commonly used in healthcare applications, to identify their strengths and limitations. By analyzing the results, researchers can gain insights into the most effective approaches for developing accurate predictive models that support decision-making during public health crises. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary COVID-19 is a global pandemic that has caused widespread illness and death. Scientists want to know how well different computer programs can predict who will die from COVID-19. They used two types of programs: ones that are good at learning patterns (classical machine learning models) and ones that are very good at understanding language (large language models). The scientists tested these programs on a large amount of data about people with COVID-19. This research can help us make better decisions to save lives during health crises. |
Keywords
* Artificial intelligence * Machine learning
