Table of Contents
- Digital Transformation of Healthcare - The Beginning
- Age of Disruption
- I love learning especially from Masters my recommended course:
- Notion PPV Life Design [❤️ Highly Recommended (Not sponsored)]
- Making machine learning trustworthy
- Mitigating bias in machine learning for medicine
- Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest
Status
Published
Video
Video
Ready to Publish
Ready to Publish
Publish Date
Sep 9, 2021
Target
Long Form
Blog Ideas
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Drafted
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Graphics
Type
Idea
Video Recording
Digital Transformation of Healthcare - The Beginning
We are living in the most interesting era of healthcare. There is a complete digital transformation of healthcare beneath our feet that is like an earthquake and we can’t feel it. We as provider need to stewards of this change!
Age of Disruption
The sudden disruptive change is deceptive. This disruption is deceptive in nature and has transformed industries it is important to understand the new Age we live and what it means for the future for healthcare.
The above videos are part of the Course:
Title: Decide Wisely - A Guide to Choosing A Medical Specialty
Section: Understanding Logarithmic to Algorithmic Change
🔗 For enrollment and details, please visit:
I love learning especially from Masters my recommended course:
Notion PPV Life Design [❤️ Highly Recommended (Not sponsored)]
Notion is my favorite productivity tool and August Bradley is the best Notion teacher that will have transformative impact on your whole life not just professional productivity. His approach of systems thinking in all arenas of life is game changing. Be sure to visit www.NotionLifeDesign.com, Follow @augustbradley, Subscribe https://www.yearzero.io/newsletter
Making machine learning trustworthy
ML must make several advances before it can be deployed with confidence in domains where it directly affects humans at training and operation, in which cases security, privacy, safety, and fairness are all essential considerations.
Mitigating bias in machine learning for medicine
Several sources of bias can affect the performance of machine learning systems used in medicine and potentially impact clinical care. Here, we discuss solutions to mitigate bias across the different development steps of machine learning-based systems for medical applications
Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest
The gradient boosted machine algorithm was the most accurate for predicting favorable neurologic outcomes in in-hospital cardiac arrest survivors. Our results highlight the utility of machine learning for predicting neurologic outcomes in resuscitated patients.
Written by
CEO | Founder | President NeuroCare.AI - Advancing Digital Health Ecosystem in Emerging Countries!