• 1. Financial and economical challenge with getting proper mental health treatment: This app offers the users to access resources and mental health professionals for free. These professionals could be volunteers.
• 2. Difficulty searching and accessing resources: Provides searching on a database of trained professionals using Algolia
• 3. Peer support Connection: "The Wall" page of the app provides a space for the users to share their thoughts anonymously, also civillians can write thank-you massages
• 4. Simple machine training algorithm: "The text analysis algorithm can be trained for better occuracy of user's risk by using more scenarios and can be trained to determine to evaluate good/bad helping."
• 5. Messaging and Text Integration: Using messaging between users and trained professionals via text.
• 6. Detection of drug abuse behaviours: Various scenarios can be presented to the user and their input can be analysed for using drug related vocabulary.

This app was inspired by LumoHacks topic of mental health issues in veterans and first responders. BritherTime has multiple functionalities such as providing a support group for the people at risk of depression while keeping it anonymous, confidential, and therapeutic. This app consits of a wall where the users can share their thoughts anonymously. The users can play a game, answer a series of questions about some real life situations and based on their answers, the app will analyze their risk of depression. This app uses machine learning to evaluate the language used in the user responses and rank them based on their risk of depression. The list of users with higher risk will be presented to a group of trained professionals where they can chose to reach out to the user via an emailing system.

Front-end programming language: React Native Back-end: Python and SQLITE3 3D animations: Blender3D for
Search: Algolia Training data and ML algorithm: From https://github.com/AshwanthRamji/Depression-Sentiment-Analysis-with-Twitter-Data Digital EngineX FLASK

• 1. A better ML algorithm can be used for text processing
• 2. Further research is needed on evaluating accuracy of depression risk based on text phrases
• 3. Other evaluation games, scenraios can be added
• 4. Counsellour and pyschologist search function can be added, database could be expanded based on area and a patient review system could be implemented
• 5. Addition of depression based on voice evaluation

IOS

Kattie Jason Nick Argenis