Boosting the Transparency of Medical Machine Learning with Amazon SageMaker Clarify: A 360-Degree Approach

Boosting the Transparency of Medical Machine Learning with Amazon SageMaker Clarify: A 360-Degree Approach

Boosting the Transparency of Medical Machine Learning with Amazon SageMaker Clarify: A 360-Degree Approach

As Seen On

As the revitalizing force of technology redefines industries, the medical sector is certainly not spared. One such technological advancement is Machine Learning (ML), a subset of artificial intelligence (AI) that is making waves across industries, and most importantly, in healthcare. Their potential applications in diagnosing diseases, predicting hospital readmission, and optimizing treatment plans are game-changing. However, their complete adoption in the medical domain faces an integral challenge – ‘Explainability.’ So, how can healthcare practitioners use ML methods confidently without a hitch? Enter Amazon SageMaker Clarify.

Amazon SageMaker Clarify is a meticulously designed tool providing a 360-degree approach to improving the explainability of ML models. This revolutionary tool enables users to have a comprehensive understanding of the accuracy of their model’s data to make key decisions about operational predictions and outcomes. However, ML model explainability needs to be understood and explained from multiple perspectives, including medical, technological, legal, and patient-based viewpoints. Despite the statistical accuracy, the ethical responsibility lies with clinicians to evaluate the strengths and weaknesses of these predictions to offer apt care for their patients.

Peering into the intricate dynamics of Clinical Decision Support Systems (CDSS) further provides valuable insight about the inner workings of the medical system. CDSSs are notably responsible for the triage process in hospitals. These crucial systems enable clinicians to prioritize patients based on the severity of their condition. At the core of these systems lie ML models, the very decisions of which are integral to patient care. Therefore, healthcare organizations must understand the influencing factors behind the ML models’ decisions.

With the disruptive force of Amazon SageMaker and SageMaker Clarify, healthcare organizations command an unprecedented power. They can now deploy predictive ML models for triage effectively within hospital settings. More importantly, they can navigate the complex mechanism of model predictions with SageMaker Clarify, promoting an exceptional level of transparency in the medical process.

Diving into the technicalities, clinical notes are valuable assets for healthcare organizations. The beauty of clinical text such as admission notes is that it pools vital information about the patient, acting as a potential goldmine for diagnostic help. Studies show impressive predictability of crucial indicators like diagnoses, procedures, length of stay, and in-hospital mortality derived from admission notes using Natural Language Processing (NLP) algorithms.

In the realm of NLP models, Bi-directional Encoder Representations from Transformers (BERT) make an indelible mark. The advanced BERT model allows for accurate predictions derived from text-based patient data. However, explaining these predictive models’ outcomes can be daunting, demanding meticulous care.

Here, SHAP, or SHapley Additive exPlanations, steps in as a dynamic model-agnostic approach for explaining the output of any ML model. Particularly for a complex model such as BERT, SHAP helps demystify the predictive decision-making process of these ML frameworks improving both the trust and confidence in their use.

As we embrace the transformative wave of machine learning technology in healthcare, the pivotal role of tools like Amazon SageMaker Clarify for boosting transparency becomes apparent. By providing insights into ML models, we empower healthcare professionals to make informed decisions in their missions to provide the best patient care. Indeed, the future of medical Machine Learning looks bright, and ever more promising.

Casey Jones Avatar
Casey Jones
10 months ago

Why Us?

  • Award-Winning Results

  • Team of 11+ Experts

  • 10,000+ Page #1 Rankings on Google

  • Dedicated to SMBs

  • $175,000,000 in Reported Client

Contact Us

Up until working with Casey, we had only had poor to mediocre experiences outsourcing work to agencies. Casey & the team at CJ&CO are the exception to the rule.

Communication was beyond great, his understanding of our vision was phenomenal, and instead of needing babysitting like the other agencies we worked with, he was not only completely dependable but also gave us sound suggestions on how to get better results, at the risk of us not needing him for the initial job we requested (absolute gem).

This has truly been the first time we worked with someone outside of our business that quickly grasped our vision, and that I could completely forget about and would still deliver above expectations.

I honestly can't wait to work in many more projects together!

Contact Us


*The information this blog provides is for general informational purposes only and is not intended as financial or professional advice. The information may not reflect current developments and may be changed or updated without notice. Any opinions expressed on this blog are the author’s own and do not necessarily reflect the views of the author’s employer or any other organization. You should not act or rely on any information contained in this blog without first seeking the advice of a professional. No representation or warranty, express or implied, is made as to the accuracy or completeness of the information contained in this blog. The author and affiliated parties assume no liability for any errors or omissions.