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The Women in Analytics committee first quarterly meeting will be presented by Kaitlin Bustos on Tackling Mental Health Using Data. Kaitlin created this post to provide a preview of her presentation and the topics she will discuss:
1 in every 8 people in the world lives with a mental disorder (1). The prevalence of mental health problems is high, and over time these numbers have remained relatively constant. Is it time to introduce a more advanced technological approach to reduce the rate of mental illness in society?
Data Science has proven its significance (no pun intended) across several domains including finance, e-commerce, gaming, manufacturing, and healthcare. The role of data science in healthcare has led to major advancements, primarily in areas such as genetics and drug discovery. Perhaps applying data science to the area of mental health could prove to be just as impactful.
The Why?
The why behind intersecting data science and mental health is to help alleviate individual suffering and in turn reduce societal burdens posed by mental illness.
The How?
Mental health data exists! We can conduct analyses, pull insights, and build predictive models using such data. My favorite dataset to explore psychological phenomena is The Collaborative Psychiatric Epidemiology Surveys (CPES). This data can allow us to gain a deeper understanding of preventing, diagnosing, and treating mental health problems.
Outlook
Unfortunately, an analysis nor a predictive model using mental health data on one’s local machine will do the trick. There needs to be some aspect of deployment. Here are some potential avenues where mental health data solutions can be implemented:
- A clinical tool
- A commercial product
- Government initiatives
What’s Next?
Dive further into this topic with me on January 19th, 2023 at the DAA’s Women in Analytics quarterly member meeting!
References:
(1) Institute of Health Metrics and Evaluation. Global Health Data Exchange (GHDx), (
https://vizhub.healthdata.org/gbd-results/, accessed 14 May 2022).