https://github.com/thbuerg/NeuralCVD

🫀 Code for "Neural network-based integration of polygenic and clinical information: Development and validation of a prediction model for 10 year risk of major adverse cardiac events in the UK Biobank cohort" 🫀
https://github.com/thbuerg/NeuralCVD

cardiovascular-disease pytorch pytorch-lightning risk-modeling survival-analysis time-to-event ukbiobank

Last synced: about 1 month ago
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🫀 Code for "Neural network-based integration of polygenic and clinical information: Development and validation of a prediction model for 10 year risk of major adverse cardiac events in the UK Biobank cohort" 🫀

• Host: GitHub
• URL: https://github.com/thbuerg/NeuralCVD
• Owner: thbuerg
• License: gpl-3.0
• Created: 2021-07-07T09:40:43.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2022-02-01T20:19:32.000Z (over 3 years ago)
• Last Synced: 2024-08-02T16:44:52.215Z (10 months ago)
• Topics: cardiovascular-disease, pytorch, pytorch-lightning, risk-modeling, survival-analysis, time-to-event, ukbiobank
• Language: Jupyter Notebook
• Homepage: https://www.thelancet.com/journals/landig/article/PIIS2589-7500(21)00249-1/fulltext
• Size: 4.09 MB
• Stars: 12
• Watchers: 2
• Forks: 4
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-uk-biobank - NeuralCVD - based integration of polygenic and clinical information of a prediction model for 10 year risk of major adverse cardiac events) | (Derivation of variables / Clinical and biochemical)

README

        
    


![Logo](./src/neuralcvd_logo.png?raw=true "Logo")

**Neural network-based integration of polygenic and clinical information: Development and validation of a prediction model for 10 year risk of major adverse cardiac events in the UK Biobank cohort**

[![Paper](https://img.shields.io/badge/TheLancet-DigitalHealth-informational)](https://www.thelancet.com/journals/landig/article/PIIS2589-7500(21)00249-1/fulltext)



 

## Description   

Code related to the paper "Neural network-based integration of polygenic and clinical information: Development and validation of a prediction model for 10 year risk of major adverse cardiac events in the UK Biobank cohort". 

This repo is a python package for preprocessing UK Biobank data and preprocessing, training and evaluating the NeuralCVD score.


![NeuralCVD](./src/neuralcvd_fig1.png?raw=true "NeuralCVD")

## Methods

**NeuralCVD** is based on the fantastic [Deep Survival Machines](https://arxiv.org/abs/2003.01176) Paper, the original implementation can be found [here](https://github.com/autonlab/DeepSurvivalMachines).

## Assets

This repo contains code to preprocess [UK Biobank](https://www.ukbiobank.ac.uk/) data, train the NeuralCVD score and analyze/evaluate its performance.

- Preprocessing involves: parsing primary care records for desired diagnosis, aggregating the cardiovascular risk factors analyzed in the study and calculating predefined polygenic risk scores.

- Training involves Model specification via pytorch-lightning and hydra.

- Postprocessing involve extensive benchmarks with linear Models, and calculation of bootstrapped metrics.

## How to train the NeuralCVD Model  

1. First, install dependencies   

```bash

# clone project   

git clone https://github.com/thbuerg/NeuralCVD

# install project   

cd NeuralCVD

pip install -e .   

pip install -r requirements.txt

 ```   

2. Download UK Biobank data. Execute preprocessing notebooks on the downloaded data.

3. Edit the `.yaml` config files in `neuralcvd/experiments/config/`:

```yaml

setup:

  project_name: /

  root_dir: absolute/path/to/this/repo/

experiment:

  tabular_filepath: path/to/processed/data

```

4. Set up [Neptune.ai](https://www.neptune.ai)

5. Train the NeuralCVD Model (make sure you are on a machine w/ GPU)

 ```bash

# module folder

cd neuralcvd

# run training

bash experiments/run_NeuralCVD_S.sh

```

## Citation   

```

@article{steinfeldt2022neural,

  title={Neural network-based integration of polygenic and clinical information: development and validation of a prediction model for 10-year risk of major adverse cardiac events in the UK Biobank cohort},

  author={Steinfeldt, Jakob and Buergel, Thore and Loock, Lukas and Kittner, Paul and Ruyoga, Greg and zu Belzen, Julius Upmeier and Sasse, Simon and Strangalies, Henrik and Christmann, Lara and Hollmann, Noah and others},

  journal={The Lancet Digital Health},

  volume={4},

  number={2},

  pages={e84--e94},

  year={2022},

  publisher={Elsevier}

}

```