Top 3 @ STEMist education hacks
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Research paper
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In an effort to streamline the process for medical practitioners and improve clinical care for patients within the hospital, we developed various machine learning and deep learning based architectures to classify healthcare conditions in a transcription note, ultimately reducing misdiagnosis rates and alleviating the burden for overloaded hospitals. Furthermore, to increase the accessibility for hospitals world-wide, our models are packaged into an efficient and accurate AI-enabled medical app.
In terms of the web technologies we used, our front-end was built with HTML, CSS, and JavaScript, and the back-end infrastructure was developed with Flask and Firebase. We implemented authentication procedures with secure login/logout functionality using Firebase and developed the database using Firebase Cloud Firestore. We believe our application is crucial for assisting clinicians in health condition diagnoses, and it has the potential of leveling the playing field between hospitals of different areas.
You may need to use pip install to install the dependencies on each line of requirements.txt (for example, pip install flask==1.1.2). This list is not comprehensive and does not include many of the modules, which will need to be installed as errors arise with running (see below section).
You will also need to install Tesseract, more details can be found online or at the Tesseract User Manual.
To run the code, open a terminal and navigate to the root directory of this repository. Then, run the following commands:
export FLASK_APP=app.py
flask run -h localhost -p 5001As import errors arise, you may need to install more modules by using pip install MODULE_NAME_HERE.
Finally, once the program runs without errors, navigate to localhost:5001 in your browser. Enjoy!