victor369basu/SagemakerHuggingfaceDashboard
This is a solution that demonstrates how to train and deploy a pre-trained Huggingface model on AWS SageMaker and publish an AWS QuickSight Dashboard that visualizes the model performance over the validation dataset and Exploratory Data Analysis for the pre-processed training dataset.
This solution helps medical professionals automatically predict diagnostic procedures for patients based on unstructured doctor's notes, or "anamnesis." It takes raw text medical transcripts as input and outputs a predicted diagnostic procedure, along with a dashboard to monitor the underlying machine learning model's performance. The primary users are doctors and administrators seeking to streamline diagnostic workflows and monitor the system's accuracy.
No commits in the last 6 months.
Use this if you need to automate the classification of medical transcripts for diagnostic prediction and want a server-side dashboard to monitor the model and data.
Not ideal if your problem domain is not medical transcript classification or if you do not use AWS SageMaker and QuickSight.
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Jupyter Notebook
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Last pushed
May 27, 2022
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