Synapse

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Inspiration

As clinical research continues to grow in demand, we wanted to create a site that could help speed up the data collection process, while maintaining accurate and unbiased results. Having heard personally from a McMaster BHSc student who conducts psychological research for the Indigenous, interviewing and surveying each and every person proves to be a slow and tedious process. Thus, we wanted to find a solution to increase efficiency in this area by streamlining the entire process, which can ultimately be beneficial for all types of medical research.

What it does

Synapse is a website that collects and gathers raw data from different people and converts this data from Web3 verified smart contracts into processed databases for clinical researchers to use. We take in and store input from the user through a form, process it, and organize it into a database that displays their user ID, age, gender, ethnicity, and sentiment analysis score (%). Depending if the user is logged in as clinical researcher, they will be able to generate and view any database they request from the search bar. Our website also has a query feature that allows the researcher to narrow down their search in the database, combine data across different tables, perform calculations, delete, or manipulate data across from the database.

How we built it

We built the frontend using React JS, the smart contract with the sepolia test net and solidity and redis and natural for the natural language processing and querying.

Challenges we ran into

We struggled to figure out how to get the web3 component to work because it was many of our first times working on a web3 project. Implementing querying using Redis was also very difficult and required us to learn a long the way.

Accomplishments that we're proud of

Being able to complete successfully complete this project. Connecting everything together was a very big hassle as we used so many different technologies simultaneously in our tech stack. It was also very difficult learning how to use Web3 for the very first time as it is a relatively unexplored technology in the previous hackathons that we have participated in.

What we learned

We learnt the struggles of actually collecting data for clinical psychology research. It is not quantifiable and is subject to a lot of bias, making experimental error very likely. Through using machine learning to synthesize varying opinions and ultimately quantify the sentiment of a patient, researchers are able to more strongly draw correlations between variables and understand the data that they are collecting.

What's next for Synapse

We hope to extend this to data processing and analysis in the natural sciences and in accelerating drug development. We further want to implement more forms of regression and more scientific tools such as the implementation of a chi-squared calculator.