Customer
Teen Line
Products and Services
Analytics
React JS
Chatbot
Microsoft Azure
Microsoft Bot Framework
Azure Virtual Machines
MS SQL
Java Microservices
Microsoft LUIS
Machine Learning
Industry
Mental Health Care
Organization Size
Corporate (51 – 200 employees)
Country
USA
It provides support, resources, and hope to young people through a hotline of professionally trained teen counsellors, and works to de-stigmatize and normalize mental health through outreach programs.
Understand and Map Teens mental health issues
Physical, emotional and social changes, including exposure to poverty, abuse, or violence, can make adolescents vulnerable to mental health problems. Protecting youth from adversity, promoting socio-emotional learning and psychological well-being, and ensuring access to mental health care are critical for their health and well-being during adolescence and adulthood.
We at Hubino helped Teen Line in developing an Artificial intelligence model to understand and map teen language to mental health issues and symptoms to better support teens. Usually the teen won’t open up during a face to face meeting about their issues they are facing. This solution have different mode of interaction like bot chat, telephonic bot and email bot which helps teen to open up and communicate freely. It has an admin panel to supervise the entire data and any human intervention can take over at any point of time.
AI Model with Machine Learning Training
Machine learning is a subset of artificial intelligence focused on building systems that can learn from historical data, identify patterns, and make logical decisions with little to no human intervention. Machine learning applications learn from the input data and continuously improve the accuracy of outputs using automated optimization methods
We at Hubino used Machine learning training to build this AI model. We have used many patients’ historical data to create this AI model and it gets trained by itself when a new set of data gets inputted. This makes the model very effective and continuous learning takes place.
Analyzing Data
Data plays a key role in treating mental illness. It is a challenge when we have a big quantity of data and a big portion of is unstructured like How we think, feel, and act which helps determine how we handle stress, relate to others, and make healthy choices. Mental health is important at every stage of life, from childhood and adolescence through adulthood.
The model is built by acquiring and analyzing data from phone calls, texts, and email datasets of a person. These data are used to train the model. It helps to manage important daily tasks that help improve and maintain their mental health.
Distinctive Modes
In mental health treatment, the sensitivity of data is high. The types of data collected for mental health are broad, ranging from mood, communication logs, and social activity to GPS data.
This model is equipped with 4 distinctive modes namely Sentimental Mode, Emergency Mode, Teen Specific, and Audio/Video Analysis Model.
Context Aware Communication
This model helps to know better about patient before attending their call. It remembers patients’ context across their many points of contact which helps to deliver proactive care.
It helps to gauge patient’s mental health issues. Ultimately leads to provide better patient care.
