During this pandemic, many hospitals are taking advantage of people's need of hospitals to get proper treatment and are quoting extremely high prices to the patients which is totally unjust. What Medic does is that it provides the patient with a transparent data such as the prices, availability of beds, oxygen and ventilator availability of all the hospitals in their locality so that the user can compare the expenses and the recovery rate of all the hospitals and choose what's best for them.
The major hurdle on the web development side was to configure the webapp with both firebase and flask and plotting graphs was also a hurdle as the Chart.js library was not upto the mark. Hosting the Flask API on various cloud services caused a problem. We tried it on various platforms such as Heroku, DigitialOcean and Linode. Dataset generation was a problem Configuring the graph data using the API was a challenge.
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- Using 'PyTorch' model to predict hospital data of the next day.
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- An interface for the hospital to update data from its database and also add and update patient medical history.
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- An interface for a patient to browse through nearby hospitals and select a hospital based on its performance, also observe his/her medical history live.
- ML
- pytorch
- numpy
- pandas
- scikit-learn
- APIs
- Firebase
- Flask
- Front-end
- Ionic Framework with React
- React Native for the patient's app
- Chart.js API
- Website
- cd hospital_side
- npm install
- ionic serve
- App
- cd patient_side
- yarn install
- react-native run-android/react-native run-ios
- ML
- backend and ML
- pip3 install -r requirements.txt
- python 3 main.py
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Breenda Das
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Shubhra Agarwal
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Palak Aggarwal
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Sumrit Grover
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