AI Mental Health App for Mood Tracking and Depression Self-Assessment

Track your mood daily with our mental health application. Get accurate depression and anxiety screening, self-assessment tools, and mood tracking in one place.

Overview

At A Glance

Industry

Healthcare

Region

USA

Duration

12 Weeks

Technical Stack

Flutter

Python

MySQL

TensorFlow

Keras

Client Profile

The client is a well-known Ivy league university based from the USA.

Challenge

According to WHO, approximately 280 million people suffer from depression around the world. Yet, 75% of them are un-diagnosed. Most people around the world do not realize that they’re suffering from mental illness, due to lack of resources/awareness.
Women are twice as likely to suffer from mental illness compared to men. However, most do not seek medical help due to lack of knowledge.

Solution

Trained an AI model with qualitative and quantitative data of mental health for analyzing the depression and anxiety levels of the user.
Users can undergo Mood rating on a scale of 1-5, which is stored on a daily basis.
Under Check-in, users can take anxiety and depression self-assessment by answering a series of scientifically approved questions. Based on those answers, the user is given anxiety and depression severity. Based on these learnings, the app suggests the user if she needs a consultation from a medical professional.
Self-assessment data is stored on a continuous basis for the ML model to learn.
If the anxiety and depression levels are high, the user can contact the crisis support helpline number or find more help under the ‘Resources’ section.
To understand the mental health progress mathematically and visually, users can view the graphs of various assessments done.
Users can put up daily task lists and reminders under the ‘Self-care’ section.

Key Benefits

Early diagnosis of mental health issues and appropriate treatment can be directed under the guidance of a medical professional.
31% of female users were able to cope up with mental health issues during their menstrual cycles and showed signs of improved happiness and overall well-being.

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