While our team has transitioned to quarantine with ease, those suffering from mental instability have struggled. With the insurmountable stress accumulating from quarantine, which can come from being laid off or dealing with an abusive family, people are simply not happy at home.
Thus, we took a unique approach to the “Happy at Home” track. While it is important to keep people happy through entertainment or productivity hacks, people who suffer from mental instability are even more vulnerable and unproductive due to shelter in place. We believe that mental stability is the root of being “Happy at Home”, and addressing those who are struggling the most is of utmost importance.
Social isolation can induce a lot of anxiety which compounds from isolation during quarantine, leaving those that suffer from depression especially vulnerable. The current economic status has prevented a huge group of people from working, which has led to declining mental states and therefore higher suicide rates. Specifically in the Bay Area, where we live, doctors have seen more deaths from suicides than COVID-19.
Facebook and other companies have created algorithms for detecting posts with suicidal sentiment. However, these solutions are insufficient because: 1) people are deterred by robots which provide generic advice and resources, and 2) they are less likely to publicly express their feelings on social media as opposed to privately speaking to their friends in-person or over text. Support and assistance from a friend instead of robots is far more ideal.
The problem is that humans are unable to accurately detect these cryptic phrases that can be considered “suicidal”, let alone distinguish them from less severe depressive sentiment. Additionally, early detection is key as the longer a person is struggling, the more likely they are to commit self-harm. We filled this critical hole through AI and ML, which can find the intricate details and differences in these messages based on comprehensive datasets.
P.S: Our name is SuiSense, which means to have a “sense” for detecting suicide.
What it does
SuiSense is a unique progressive web application that allows concerned family and friends to determine whether their struggling loved one is on the path to suicide. The core of our project is a Natural Language Processing model(sci-kit) that classifies a phrase someone says as representing suicidal tendencies, depression, or neither. Users can input text from their messaging conversations, memory of their in-person conversations, letters, diary entries, or screenshots (powered by TessaractOCR). Classifying between suicide and depression is important because the implications and methods for support are completely different, but determining the difference is a precise task that is best dealt with through ML.
Through extensive research, we were able to find a dataset from Medium which web-scraped reddit with thousands of posts from “r/depression” and “r/SuicideWatch”. We managed, trained, and improved our model using automations from UIPath AI-Fabric. We also created a web-scraper to periodically collect data from the subreddits to improve our classifier. We deployed all our models using Google Cloud , UIPath, and PythonAnywhere.
After our model determines a primary classification, we then feed data into our progression model. Psychologist Jesse Bering discovered six stages on the path from depression to suicide, and our model accurately assesses what stage a patient is at and the severity of their depression. This model is trained on metadata containing attributes such as gender, race, employment status, and more.
Next, our framework allows friends and family to find the best therapists to address their specific needs. Our algorithm uses the data from this form along with key words from the original classification to match a user with a therapist that is best suited for them. We store all user data and classifications in Firebase from Google Cloud.We also have local therapists and support centers displayed on a Map, powered by the Google Cloud Maps API.
Lastly, we have a support page that allows therapists and patients to post on a forum, similar to a blog, creating a safe community for those who feel lonely and isolated.
How we built it
After numerous hours of wireframing, conceptualizing key features, and outlining tasks, we divided the challenge amongst ourselves by assigning Adithya and Ishaan to developing and designing the UI/UX, Ayaan to building the NLP model and training, improving, and testing the model using UIPath AI-Fabric automations, and Viraaj to integrate Google Cloud Functions, TessaractOCR, and our python webapp.
Challenges we ran into
The primary challenge that arose for us was training and deploying of our model. Since this was our first NLP project, we struggled to train and make predictions with NLP models as our model utilized too much data and local machine power . Luckily, we found AI-Fabric from UIPath, which automated the training and testing process for our model. UIPath also was integrated into our web app to optimize our deployment process.
Accomplishments we are proud of
We are incredibly proud of how our team found a distinctive yet foundational solution to those who are struggling with mental instability due to external pressures, especially from COVID-19. We feel accomplished because of how efficiently we built off our specific research from various sources and applied them in a technological context to build one comprehensive product. We are extremely proud of developing a solution that has not been previously considered in this setting and putting it into functional form.
What we learned
Our team found it incredibly fulfilling to use our Machine Learning knowledge in a way that could effectively assist people struggling with their mental health, especially during such a strenuous time where their existing difficulties are amplified. Seeing how we could use our software engineering skills to impact people who are often ignored and stigmatized in society was the highlight of our weekend.
From a software perspective, learning NLP was our main focus this weekend, as it was our next step in our ML journey. Most importantly, learning AI-Fabric from UIPath was a phenomenal method for automating our training, testing, and deployment of our machine taxing models, and we will continue to use UIPath for future ML automations.
What is next for SuiSense
We aim to integrate SuiSense in schools and workplaces. These are high pressure environments where people’s mental health is gradually deteriorating; we feel that our software will allow compassionate friends to accurately recognize their peers’ struggles and provide them with personalized and trustworthy support.
For our application, we want to integrate more UIPath automations for our algorithm and website because it made the process incredibly easy. We also want to improve our progression model to better classify and diagnose depression and suicidal progression in patients, which would allow therapists or friends to better understand who they are trying to help.
Demo(if youtube isn’t working): https://drive.google.com/file/d/1FbF8uynkRqq9fHK7epFwzSeWaRmB3G-7/view?usp=sharing