Mjaye Mazwi
Orcid: 0000-0003-1345-5429
According to our database1,
Mjaye Mazwi
authored at least 16 papers
between 2017 and 2024.
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Bibliography
2024
Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Waveform Data.
CoRR, 2024
2023
iCVS - Inferring Cardio-Vascular hidden States from physiological signals available at the bedside.
PLoS Comput. Biol., 2023
Making machine learning matter to clinicians: model actionability in medical decision-making.
npj Digit. Medicine, 2023
RiskFix: Supporting Expert Validation of Predictive Timeseries Models in High-Intensity Settings.
Proceedings of the 25th Eurographics Conference on Visualization, 2023
What's fair is... fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning: JustEFAB.
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023
2022
Frontiers Digit. Health, 2022
Frontiers Digit. Health, 2022
Proceedings of the Conference on Health, Inference, and Learning, 2022
How to validate Machine Learning Models Prior to Deployment: Silent trial protocol for evaluation of real-time models at ICU.
Proceedings of the Conference on Health, Inference, and Learning, 2022
Get To The Point! Problem-Based Curated Data Views To Augment Care For Critically Ill Patients.
Proceedings of the CHI '22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April 2022, 2022
2020
Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning.
J. Am. Medical Informatics Assoc., 2020
Rhythm Classification of 12-Lead ECGs Using Deep Neural Networks and Class-Activation Maps for Improved Explainability.
Proceedings of the Computing in Cardiology, 2020
When Your Only Tool Is A Hammer: Ethical Limitations of Algorithmic Fairness Solutions in Healthcare Machine Learning.
Proceedings of the AIES '20: AAAI/ACM Conference on AI, 2020
2018
Proceedings of the Machine Learning for Healthcare Conference, 2018
Towards Understanding ECG Rhythm Classification Using Convolutional Neural Networks and Attention Mappings.
Proceedings of the Machine Learning for Healthcare Conference, 2018
2017
Classification of Atrial Fibrillation Using Multidisciplinary Features and Gradient Boosting.
Proceedings of the Computing in Cardiology, 2017