Summary of Predicting Heart Failure with Attention Learning Techniques Utilizing Cardiovascular Data, by Ershadul Haque et al.
Predicting Heart Failure with Attention Learning Techniques Utilizing Cardiovascular Data
by Ershadul Haque, Manoranjan Paul, Faranak Tohidi
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper presents a novel attention learning-based approach for predicting heart failure using electronic health record (EHR) data, specifically focusing on ejection fraction and serum creatinine. The method leverages different optimizers with varying learning rates to fine-tune the model. Results show that RMSProp optimizer with a 0.001 learning rate performs well in predicting heart failure based on serum creatinine, while SGD optimizer with a 0.01 learning rate is optimal for ejection fraction features. This approach outperforms state-of-the-art models like LSTM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors predict when someone will get very sick or even die from heart failure. It uses special computer programs to look at patient records and find important clues, like how well the heart is working. The researchers tried different ways of making these programs work better and found that one combination works best for looking at some types of data and another combination works best for looking at other types of data. This can help doctors give patients the right treatment to prevent or treat heart failure. |
Keywords
* Artificial intelligence * Attention * Lstm
