https://github.com/ai4healthuol/cardiodiag

This is the official repository for CardioDiag. A machine learning framework for the diagnosis of non-cardiac conditions.

https://github.com/ai4healthuol/cardiodiag

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Repository

This is the official repository for CardioDiag. A machine learning framework for the diagnosis of non-cardiac conditions.

Basic Info
  • Host: GitHub
  • Owner: AI4HealthUOL
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 28.3 KB
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  • Stars: 4
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 2 years ago · Last pushed about 1 year ago
Metadata Files
Readme

README.md

This is the official repository for CardioDiag. An externally validated and explainable machine learning framework for the diagnoses of diverse non-cardiac conditions.

CardioDiag have been proposed in four main manuscrips:

  1. Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Features Accepted by the international conference of computing in cardiology (CinC) 2024. arXiv

  2. Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approach arXiv

  3. Explainable machine learning for neoplasms diagnosis via electrocardiograms: an externally validated study arXiv

  4. Neuropsychiatric arXiv

In terms of input features, we use demographics such as age and gender, as well as ECG features such as RR-interval, PR-interval, QRS-duration, QT-interval, QTc-interval in milliseconds, P-wave axis, QRS-axis, as well as T-wave axis in degrees. All of the diagnoses are well-defined diagnoses by means of ICD10-CM codes.

References

bibtex @misc{alcaraz2024estimationcardiacnoncardiacdiagnosis, title={Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Features}, author={Juan Miguel Lopez Alcaraz and Nils Strodthoff}, year={2024}, eprint={2408.17329}, archivePrefix={arXiv}, primaryClass={eess.SP}, url={https://arxiv.org/abs/2408.17329}, }

bibtex @misc{alcaraz2024electrocardiogrambaseddiagnosisliverdiseases, title={Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approach}, author={Juan Miguel Lopez Alcaraz and Wilhelm Haverkamp and Nils Strodthoff}, year={2024}, eprint={2412.03717}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2412.03717}, }

bibtex @misc{alcaraz2024explainablemachinelearningneoplasms, title={Explainable machine learning for neoplasms diagnosis via electrocardiograms: an externally validated study}, author={Juan Miguel Lopez Alcaraz and Wilhelm Haverkamp and Nils Strodthoff}, year={2024}, eprint={2412.07737}, archivePrefix={arXiv}, primaryClass={eess.SP}, url={https://arxiv.org/abs/2412.07737}, }

bibtex @misc{alcaraz2025explainable, title={Explainable and externally validated machine learning for neuropsychiatric diagnosis via electrocardiograms}, author={Alcaraz, Juan Miguel Lopez and Oloyede, Ebenezer and Taylor, David and Haverkamp, Wilhelm and Strodthoff, Nils}, year={2025}, eprint={2502.04918}, archivePrefix={arXiv}, primaryClass={eess.SP}, url={https://arxiv.org/abs/2502.04918}, }

Owner

  • Name: AI4HealthUOL
  • Login: AI4HealthUOL
  • Kind: organization
  • Location: Germany

Public repositories of the AI4Health Division at Oldenburg University

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