# Optimizing Medication Decisions for Patients with Atrial Fibrillation through Path Development Network

arXiv: https://arxiv.org/abs/2401.10014 · HTML: https://mlsanity.com/paper/2401.10014/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 110 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 16 of 16)

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- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Understanding the Effective Receptive Field in Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.04128/) (2017)
- [On the Expressive Power of Deep Neural Networks](https://mlsanity.com/paper/1606.05336/) (2016)
- [Benefits of depth in neural networks](https://mlsanity.com/paper/1602.04485/) (2016)
- [Riemannian Adaptive Optimization Methods](https://mlsanity.com/paper/1810.00760/) (2018)
- [Hyper-Parameter Optimization: A Review of Algorithms and Applications](https://mlsanity.com/paper/2003.05689/) (2020)
- [A Primer on the Signature Method in Machine Learning](https://mlsanity.com/paper/1603.03788/) (2016)
- [Multi-Scale Convolutional Neural Networks for Time Series Classification](https://mlsanity.com/paper/1603.06995/) (2016)
- [Trivializations for Gradient-Based Optimization on Manifolds](https://mlsanity.com/paper/1909.09501/) (2019)
- [Path Development Network with Finite-dimensional Lie Group Representation](https://mlsanity.com/paper/2204.00740/) (2022)
- [Adaptive and Momentum Methods on Manifolds Through Trivializations](https://mlsanity.com/paper/2010.04617/) (2020)

## Abstract (arXiv)

Atrial fibrillation (AF) is a common cardiac arrhythmia characterized by rapid and irregular contractions of the atria. It significantly elevates the risk of strokes due to slowed blood flow in the atria, especially in the left atrial appendage, which is prone to blood clot formation. Such clots can migrate into cerebral arteries, leading to ischemic stroke. To assess whether AF patients should be prescribed anticoagulants, doctors often 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. In this model, we use STOME to enhance time-series data and then process it through a Convolutional Neural Network (CNN). By incorporating a path development layer, the model achieves a specificity of 30.6% under the condition of an NPV of 1. In contrast, LSTM algorithms without path development yield a specificity of only 2.7% under the same NPV condition.