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Applied Machine Learning for Healthcare and Life Sciences using AWS

You're reading from   Applied Machine Learning for Healthcare and Life Sciences using AWS Transformational AI implementations for biotech, clinical, and healthcare organizations

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Product type Paperback
Published in Nov 2022
Publisher Packt
ISBN-13 9781804610213
Length 224 pages
Edition 1st Edition
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Author (1):
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Ujjwal Ratan Ujjwal Ratan
Author Profile Icon Ujjwal Ratan
Ujjwal Ratan
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Table of Contents (19) Chapters Close

Preface 1. Part 1: Introduction to Machine Learning on AWS
2. Chapter 1: Introducing Machine Learning and the AWS Machine Learning Stack FREE CHAPTER 3. Chapter 2: Exploring Key AWS Machine Learning Services for Healthcare and Life Sciences 4. Part 2: Machine Learning Applications in the Healthcare Industry
5. Chapter 3: Machine Learning for Patient Risk Stratification 6. Chapter 4: Using Machine Learning to Improve Operational Efficiency for Healthcare Providers 7. Chapter 5: Implementing Machine Learning for Healthcare Payors 8. Chapter 6: Implementing Machine Learning for Medical Devices and Radiology Images 9. Part 3: Machine Learning Applications in the Life Sciences Industry
10. Chapter 7: Applying Machine Learning to Genomics 11. Chapter 8: Applying Machine Learning to Molecular Data 12. Chapter 9: Applying Machine Learning to Clinical Trials and Pharmacovigilance 13. Chapter 10: Utilizing Machine Learning in the Pharmaceutical Supply Chain 14. Part 4: Challenges and the Future of AI in Healthcare and Life Sciences
15. Chapter 11: Understanding Common Industry Challenges and Solutions 16. Chapter 12: Understanding Current Industry Trends and Future Applications 17. Index 18. Other Books You May Enjoy

Viewing bias and explainability reports in SageMaker Studio

After completing the steps in the preceding section, SageMaker Clarify creates two reports for you to examine. The first report allows you to look at the bias metrics for your dataset and model. The second report is the explainability report, which tells you which features are influencing the model predictions. To view the reports, follow these steps:

  1. Click on SageMaker resources in the left navigation pane of SageMaker Studio. Make sure Experiments and trials is selected in the drop-down menu:

Figure 11.3 – SageMaker Studio interface showing the SageMaker resources button

  1. Double-click on Unassigned trial components at the top of the list. In the next screen, you will see two trials. The first trial starts with clarify-explainability and the second starts with clarify-bias.
  2. Double-click on the report starting with clarify-bias. Navigate to the Bias report tab, as shown in...
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