DSM-5 and SAMHSA-informed expert support and crisis care
Project submitted to the UC Berkeley AI Hackathon (June 22 - 23, 2024 @ UC Berkeley). View Devpost for more details: https://devpost.com/software/neurosense-8ykswz
NeuroSense was inspired by the skyrocketing demand for accessible mental health support and the potential of AI to bridge this gap. Our goal was to leverage advanced large language models to provide reliable and immediate mental health assistance, guided by established and consistent clinical guidelines.
NeuroSense is a virtual assistant powered by large language models, designed to offer mental health support and behavioral crisis care. It integrates clinical knowledge from the DSM-5 and SAMHSA's National Guidelines for Behavioral Health Crisis Care Best Practice Toolkit, two of the most reputable manuals for psychiastrists. NeuroSense provides users with expert advice, crisis intervention, and resources, all through an intuitive web application.
We developed NeuroSense using state-of-the-art natural language processing techniques. The chatbot is trained on the DSM-5 and SAMHSA guidelines, ensuring it provides accurate and contextually appropriate responses. We utilized robust machine learning frameworks and APIs to enhance the chatbot's understanding and interaction capabilities, with LangChain powering the backend and Flask building out the frontend.
(1) Integrating comprehensive clinical guidelines into the chatbot without losing critical nuances. (2) Ensuring the chatbot's responses are both empathetic and clinically sound. (3) Managing data privacy and user confidentiality in compliance with healthcare regulations.
(1) Successfully integrating DSM-5 and SAMHSA guidelines into a functional chatbot. (2) Achieving a high level of accuracy and sensitivity in the chatbot's responses. (3) Developing a tool that can potentially provide immediate support to individuals in crisis.
(1) The complexities of encoding clinical knowledge into AI systems. (2) The importance of balancing technical accuracy with human empathy in mental health applications. (3) Insights into user privacy concerns and how to address them in healthcare technology.
(1) Expanding the chatbot's knowledge base to include more diverse mental health resources. (2) Collaborating with mental health professionals for continuous improvement and validation. (3) Exploring partnerships with healthcare providers to integrate NeuroSense into existing support systems.
- css3
- flask
- html5
- javascript
- langchain
- open-ai
- pypdf
- python
- visual-studio