Team Members

1. Alaukika Diwanji
2. Dhruwaksh Dave
3. Panth Desai
4. Udit Marolia

Many valuable information regarding the public health and welfare, disease outbreaks and their trend are available in the form of unstructured data lying in different news portals, Facebook, Twitter. It becomes important to become aware of the current diseases and to filter out relevant and correct information. This is especially important for commercial pharmacies as their need to be updated with the current outbreak in their region and also be ready stock-wise for the drugs needed to treat them. Our objective with Well-Pharma is to address this problem and built a system for the pharmacies which will analyse the disease outbreaks in all regions and carry out a disease-to-drug mapping and alert the pharmacist so as to keep the stock ready.

WellPharma will be a Web Application - built as an automated system for querying filtering and visualising the disease outbreak and to stock their respective drugs.

The main target user for the application will be the commercial pharmacies. The system will provide insights to the pharmacists on which drugs need to be stocked as per the current disease outbreak.

The system will provide the pharmacists drugs recommendations as per the diseases prevelant in the selected region so that they can be prepared with those OTC drugs.

The main objective of the system will be to gather information regarding the disease outbreaks, location-wise through web crawling the CDC site to obtain disease data. Another crawler will crawl the internet for mapping drugs with diseases. This file will be fed into a python program that converts it into a timeseries form which will be fed into Facebook Prophet. It gives us the prediction about January 20. The top 5 diseases in January 20 are displayed on the website. The user(pharmacist) requests states his location, based on which the system provides the diseases prevalent in the region and the drugs corresponding to them. The data can help identify which drugs are needed to be stocked such that there is no shortage.

Web Application

The Web Application will be the main interface to the users. The application will be built using the JS tools

Frontend: ReactJS will be used for the GUI.It will also take care of the authentication and token passing.

Backend: NodeJS and NPM Libraries will be used for backend. It will communicate with MySQL database to fetch the queried. Data

Cloud: The web application will be hosted on Amazon AWS using Docker container

Database

MySQL database will be used to store all data, which is : a) The data from the classification model, which will give information about location wise disease outbreak b) The disease to drug mapped data c) The web application data

Data Model

Data Cleaning and preprocessing: Python will be used to extract the data from various public web sources and clean the data.

NLP /NER: Python,NLTK, Tensorflow will be used for classification of the data location wise so as the location and the diseases prevalent there are identified and mapped.

Professor Ranjan's initial feedback

You must clearly define the personas. If this is for the health care provider or govt agency then gathering drug sale data from all pharmacy in a zipcode(for ex) is tricky. sales data of drugs at local level may not be available publicly. They wont share. large pharma companies may share their sales data but that may not tell you the ground truth. Also in developing countries like india majority of drug sales are not tracked due to tax evasion. Here is the twist to the problem I am giving you. You apply NLP and NER on all the public news in a geography or zipcode to figure out the health related problems and then give recommnedation to local pharmacist to stock those OTC drugs. You can also extend this to beauty products based on the beauty trends but beauty trends are more global than local. Weather data can play role as well.