Inspiration
There is a persistent problem of medical devices leading to adverse events.
What it does
Identify patterns in procedure/device-related errors in adverse events associated with medical device usage.
How we built it
We used python and its data visualization libraries like matplotlib and seaborn to perform exploratory data analysis and create machine learning models.
Challenges we ran into
We couldn't train the machine learning model on entire dataset due to the low RAM size, hence we had to sample some part of the data and proceed with the modeling.
Accomplishments that we're proud of
Collaborating to complete EDA, Data Processing and Modeling to use the time available efficiently. Analyzing the predictor of adverse events when using medical devices on patients.
What we learned
Extracting and cleaning real world data.
What's next for MediSafe: Analysing Safety of Medical Devices & Procedures
Further deep dive analysis into feature engineering.
Built With
- matplotlib
- python
- seaborn
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