Summary of Controlling For Unobserved Confounding with Large Language Model Classification Of Patient Smoking Status, by Samuel Lee and Zach Wood-doughty
Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Status
by Samuel Lee, Zach Wood-Doughty
First submitted to arxiv on: 5 Nov 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 proposed method leverages machine learning and large language models to predict patients’ smoking status from clinical notes, addressing unobserved confounding in observational data. By correcting for measurement errors in the predicted smoking status, the study estimates the causal effect of transthoracic echocardiography on mortality using the MIMIC dataset. This work extends prior methodologies by applying these techniques to real-world datasets and complex variables. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers called machine learning models to help doctors understand how different treatments work. The problem is that sometimes, important information isn’t recorded in a patient’s medical notes. To solve this, the researchers trained a big language model on lots of medical texts to guess whether patients smoked or not. This helps remove some of the unknown factors that could be affecting the results. They then used this model to study how a certain test called echocardiography affects people’s chances of dying. The goal is to give doctors more accurate answers about what works best. |
Keywords
* Artificial intelligence * Language model * Machine learning
