A Deep Dive into Mental Health Data Science

Last month, the MQ and DATAMIND data science meeting was held in Swansea.

DATAMIND Hub expands the benefits of data by securely combining data from multiple sources such as medical records, schools, administration, charities and more. Then, through the UK Health Data Research Innovation Portal, researchers can discover the data and use it more effectively so that we can eventually together achieve the next advances in mental health science that lead to a mentally healthier future for all of us.

Harshal Lohakare (pictured above) is a passionate mental health data scientist who attended the event in Wales, as well as the associated workshop the day before. Here, he kindly shares what it was like to attend.

Day 1: Research Workshop on Machine Learning in Mental Health

For this two-day event, students, researchers, clinicians, and industry people interested in mental health data science came together for an immersive and insightful event. With a focus on leveraging data science techniques to understand and address mental health challenges, participants delved into machine learning workshops, talks from guest speakers, insights from people with lived experience, and captivating presentations from early career researchers ( ECR). Let’s take a tour of the highlights of this enriching event.

The MQ & DATAMINDS workshop began on a warm, sunny day at Swansea University with a brief welcome and introduction given by Professor Ann John to all participants and a heartfelt tribute to Lea Milligan, CEO of MQ, who passed away after a Sudden illness on Monday, April 15.

The event began with a dynamic interactive workshop aimed at introducing participants to the fundamentals of machine learning in the context of mental health, led by senior lecturer Dr. Marcos Del Pozo Baños. He took us on a step-by-step journey of how great machine learning models are created and explained each step in detailed and simple terms. Personally, having a basic understanding of machine learning in this workshop helped me understand the big picture of machine learning modeling and, in particular, the mathematics behind it.

We learned about various machine learning algorithms and their applications for analyzing mental health data. Through hands-on exercises and interactive discussions, we gained valuable insights into how machine learning can contribute to our understanding of mental health issues and inform evidence-based interventions. I would highly recommend it to someone who is trying to dive into the world of machine learning and mental health, without having any prior knowledge about it.

Day 2: Guest speaker talks and panel discussions

The second day of the event was filled with inspiring talks from renowned guest speakers who are leading figures in the field of mental health data science. These experts shared their cutting-edge research findings, innovative methodologies, and practical implications for improving mental health outcomes using data-driven approaches.

They showed us the large number of projects underway and about to begin in the field of mental health data science and the large number of data sets available for longitudinal and cohort linkage and the data sets available and waiting to be explored. This also showed us the vast number of opportunities available to explore the field of serious mental illness, mental health disparities, and the use of administrative data in mental health research.

These presentations were followed by an all-speaker panel discussion on societal perspectives on mental health data science, which generated many interesting discussions in the room ranging from accountability or researchers and policy makers , to the effective translation of research into policy formulation and without exaggeration. the results of your research. Discussions about data privacy, public concerns about their data use, and transparency about how public data is used for research and the impact it is generating.

Additionally, the importance of considering and implementing biopsychosocial models in mental health data science research and other considerations when writing a grant proposal. One of the panel members for this discussion was a person with lived experience, highlighting and providing insight into the gap that exists between researchers and people living with these mental conditions, and how important it is to close it.

Highlights: ECR Presentations

One of the highlights of the second day was exceptional presentations from early career researchers (ECRs) in mental health data science. These emerging scholars showcased their innovative research projects, demonstrating creativity, rigor and passion in addressing pressing mental health challenges.

ECR presentations offered new perspectives and innovative solutions to complex problems, from new data collection methods to linkages and advanced statistical analysis. Attendees were impressed by the depth of knowledge and quality of research presented by these talented ECRs, highlighting the promising future of mental health data science.

Additionally, I had the exciting opportunity to present my research poster entitled “Self-harm contacts in healthcare settings in Wales: an electronic cohort study using routinely collected linked healthcare data”. It was also encouraging to engage in vibrant discussions and receive valuable feedback.

Key takeaways

• Participants gained fundamental knowledge of machine learning techniques applicable to mental health research.
• Talks from guest speakers provided insights into cutting-edge research and practical applications of mental health data science.
• ECR presentations showcased the next generation of talent and innovation, inspiring collaboration and future research efforts.

The Mental Health Data Science event was a resounding success, bringing together researchers, practitioners and enthusiasts to explore the intersection of data science and mental health. As we reflect on the insights gained and connections made over these two days, we feel motivated to continue our journey toward leveraging data science to improve mental health outcomes.

Thanks to Harshal Lohakare for sharing his insights.