Summary of Optimizing Medication Decisions For Patients with Atrial Fibrillation Through Path Development Network, by Tian Xie
Optimizing Medication Decisions for Patients with Atrial Fibrillation through Path Development Network
by Tian Xie
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 study presents a machine learning algorithm that predicts whether patients with atrial fibrillation (AF) should be prescribed anticoagulant therapy using 12-lead electrocardiogram (ECG) data. The model enhances time-series data using STOME and processes it through a Convolutional Neural Network (CNN) with a path development layer, achieving a specificity of 30.6% under the condition of an NPV of 1. This outperforms LSTM algorithms without path development, which yield a specificity of only 2.7% under the same condition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AF is a common cardiac arrhythmia that increases the risk of strokes due to slowed blood flow in the atria. To determine whether AF patients should be prescribed anticoagulants, doctors use the CHA2DS2-VASc scoring system. However, anticoagulant use must be approached with caution as it can impact clotting functions. This study introduces a machine learning algorithm that predicts whether patients with AF should be recommended anticoagulant therapy using 12-lead ECG data. |
Keywords
* Artificial intelligence * Cnn * Lstm * Machine learning * Neural network * Time series
