Patient Diagnosis and Drug Recommendation System for certain diseases using sentiment analysis of drug reviews
The Patient Diagnosis and Drug Recommendation System is an intelligent software application that utilizes symptom inputs to accurately diagnose diseases and provide top drug recommendations. The system analyzes patient symptoms and applies sentiment analysis to drug reviews to recommend the best drugs for treatment. With this system, healthcare providers can quickly and efficiently diagnose and treat patients with confidence.
The dataset provides patient reviews on specific drugs along with related conditions and a 10 star patient rating reflecting overall patient satisfaction. The data was obtained by crawling online pharmaceutical review sites. The intention was to study.
The data is split into a train (75%) a test (25%) partition (see publication) and stored in two .tsv (tab-separated-values) files, respectively.
- Set up a virtual environment: Create a virtual environment to keep your project's dependencies separate from your system's dependencies. You can use tools like virtualenv or conda to create a virtual environment.
pip install virtualenv
virtualenv <my_env_name>
source <my_env_name>/bin/activate
- Install dependencies: Once you have set up your virtual environment, install the required dependencies for your project using a package manager like pip or conda. You can either install them all at once by using a requirements.txt file or manually install them one by one.
pip install -r requirements.txt
- Download models:You may need to run the notebook file in jupyterlab to obtain the models.
Run the project using following command
streamlit run app.py