A thriving medical Company needed further development for its algorithm for stimulating the airway when sleep apnea events occurred. They wanted to figure out how much they could improve their algorithm with machine learning & AI to create a better solution.
phData experimented with a variety of machine learning methods to determine what approaches would be viable in providing improvement to the existing algorithm. In order to do this, phData build an MLOps platform in AzureML to enable the training of various advanced modeling techniques, such as Long Short Term Memory (LSTM), Convolutional Neural Networks (CNN), Fully Convolutional Networks (FCN), XGBoost, and TapNet.
A Medical company sought to find a way to predict sleep apnea episodes using a machine learning approach by improving their algorithm with deep machine learning. Due to limited resources on their internal team, they turned to phData for help.
In just 13 weeks, phData was able to test a variety of approaches and algorithms to determine which combinations would help improve the function of the medical device. phData provisioned a machine learning platform within Azure to enable a repeatable MLOps process.
With that platform, phData was able to experiment with a variety of models that learned how to forecast when a person was inhaling to guide sleep apnea intervention. This process allowed phData and the customer to evaluate performance of the models with unseen sleep data to compare performance with the customer’s existing approach.
The customer chose to work with phData because of our broad expertise and leadership in machine learning.
Upon completion of phData’s engagement with the customer, phData set up a machine learning platform and built models that would allow the customer to see what improvement could happen if both approaches were combined.
Additionally, phData came with a creative approach to the customer’s problem, which gave the customer options in furthering their development through best practices within the machine learning/AI space.
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