BrahimFakri/Patient-Health-Data-Analysis-And-Feature-Extraction-For-Machine-Learning
Embeddings generation from MIMIC-IV and MIMIC-CXR
This project helps medical researchers transform raw patient data into structured feature sets suitable for machine learning. It takes multimodal data from the MIMIC-IV and MIMIC-CXR datasets, including tabular, time-series, and image data, and generates consolidated patient embeddings. Clinical researchers, data scientists in healthcare, or biomedical engineers can use these outputs to build predictive models or analyze patient outcomes.
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Use this if you need to create a unified, machine-learning-ready dataset from diverse MIMIC patient records for tasks like predicting disease progression or treatment response.
Not ideal if you are working with patient notes data, as text embeddings are not generated, or if you need to incorporate vision probability calculations.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Dec 03, 2023
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curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/BrahimFakri/Patient-Health-Data-Analysis-And-Feature-Extraction-For-Machine-Learning"
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