Disclaimer: This is a personal project currently in development, and is not a real, certified clinical application. Do not treat it as such.
Using Artificial Intelligence to distinguish between suicidal and depressive messages.
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4th Place @ Congressional App Challenge 2020
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2nd Place Overall @ GeomHacks 2020
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Honorable Mention @ MLH Summer League SHDH 2020
Demo: https://www.youtube.com/watch?v=QHpKJBVObhA
Medium Article: https://codeburst.io/suisense-an-innovative-approach-to-suicide-prevention-19cbdf150575
SuiSense is a progressive web application that uses Artificial Intelligence (AI) and Natural Language Processing (NLP) to distinguish between depressive and suicidal phrases and help concerned friends and family determine whether their struggling loved one is on the path to suicide.
SuiSense provides 4 key services to do so. The first, our Initial Screening model, determines whether provided phrases are representative of depressive or suicidal tendencies. Friends and family can input concerning texts and receive a classification on whether they are depressive or suicidal. Especially during the pandemic, it is essential to utilize online messages to classify patients. An algorithm classifying depression versus suicide is incredibly important, as treatment methods differ significantly. Second, our Baseline Screening allows users to determine how much someone has progressed towards suicidality. Users upload 3 messages before and after a change was noticed, and our algorithm calculates the percent change towards suicidality based on the texts, which allows for direct comparison between the messages. Thirdly, our Progression Screening classifies depression and suicide based on psychologist Jesse Bering’s 6 stages of depression, a standard psychology metric. Our model fills a vital gap; there is no research in stage-based depression analysis despite its demand. Users upload concerning texts and discover which stage their loved one is on. Finally, our support page allows users to upload key information about their loved one so they can be paired with relevant therapists.
Our algorithms are trained on thousands of social media posts, specifically from the subreddits r/SuicideWatch, r/Depression, and r/CasualConversations. Field research has proven that people are likely to express their feelings on anonymous platforms, making it a great place to access data. After cleaning and labeling, we input it into Google’s BERT transformer, an NLP neural network that outputs word embeddings with the context of sentences. We trained three different Keras neural networks on three datasets. Facebook’s algorithm achieves 72% accuracy, but due to our unique approach, all our models range from 81% to 90% accuracy, proving their viability and effectiveness.
We believe the most effective way to create a useful solution is to implement our target audiences’ input. We first reached out to Dr. Marcille, former President of the California Psychological Association, and as our consultant, he has helped our solution address real problems therapists and families face. Our advisor Dr. Ruebsamen, a licensed Bay Area psychologist, has supported our solution and is helping us market it with therapists. We also partnered with CAPA and MHI, two local nonprofits working to improve mental health and raise awareness, through whom we have begun implementing our solution. SuiSense addresses pressing mental health issues that impact millions in the Bay Area, especially during COVID-19, and we have already begun implementing our solution to maximize our community impact.