Summary
Mental Health Hero is a graph-based approach to solving one of the largest public health epidemics today: the prevalence of untreated mental health issues such as anxiety and depression.
Inspiration
Only 40% of adults that suffer from mental illness ever seek treatment, so Mental Health Hero was built to solve the prevalence of untreated mental health issues such as anxiety and depression. To prepare for the challenge, the winners spoke with 10 psychologists and learned that the underlying issue in increasing access to the appropriate mental health treatment is about getting a holistic view of a (potential) patient from a variety of different touchpoints. Mental Health Hero has the potential to help hundreds of millions of people around the world. The CDC estimates that 400 million people globally are not receiving essential treatment for their mental health disorders.
What it Does
Mental Health Hero tackles these issues by using graph databases to create a holistic view of someone from multiple modalities like social media sentiment, therapy session adherence, and written entries. By doing this, we can identify who needs help and what their ideal treatment could be by comparing them with similar patients. Solving the similarity problem would help many stakeholders in this space: therapists can adjust their care for their patients, insurance companies can see who needs preventative help from their customer base, and governments can create public health programs to categorize their patient population and provide them the appropriate care.
TigerGraph was used to build a “Patient 360” view of a potential mental healthcare patient using multiple modalities like social media sentiment, therapy session adherence, and written entries. Similarity algorithms were leveraged to recommend the appropriate care based on the outcomes of other patients that had similar behaviors.
How We Did It
Multiple features of TigerGraph were used:
- The Customer 360 model was used to create a holistic view of a patient.
- Numerous similarity measures to help find related patients, including Fast RP embedding. They ended up using a modified version of the Jaccard similarity measure.
- For all the above, they heavily dug into GSQL and became quite proficient over the course of the hackathon.
