Crisis Response Network

Model Reacting to a non suicidal message
Model Reacting to a non suicidal message and providing support

Inspiration

We were inspired by the need for timely intervention in suicide prevention, leveraging technology to offer support.

What it does

Our Crisis Response Network employs logistic regression to predict suicidal tendencies from text inputs, offering assistance to at-risk users.

How we built it

We constructed our network by implementing logistic regression models trained on relevant datasets, focusing on text analysis for risk assessment. This approach created a pure machine learning model based on logistic regression which we named PreventSuicideLogit

Challenges we ran into

One major challenge was the delay in training our BERT model, prompting us to swiftly pivot to logistic regression to meet project deadlines.

Accomplishments that we're proud of

Despite setbacks, we successfully deployed a functional model, ensuring rapid identification and support for individuals in crisis.

What we learned

We learned the importance of adaptability in project development, prioritizing effective solutions over intricate models for immediate impact.

What's next for Crisis Response Network

Moving forward, we aim to integrate advanced NLP techniques and enhance our model's accuracy, expanding our outreach and effectiveness in suicide prevention efforts.