Reflections on the MQ DATAMIND Assembly 2025

This weblog was written for MQ by Dr Amy Ronaldson.

In Could 2025, the MQ DATAMIND Knowledge Science biannual assembly passed off on the London HQ of Deutsche Financial institution. As an MQ Analysis Fellow, I used to be excited to attend and acquire insights from main researchers, clinicians, coverage makers, and people with lived expertise of psychological well being challenges.

About me

I’m Amy, an MQ Analysis Fellow utilizing giant quantities of routinely collected well being information to grasp an infection outcomes in folks with extreme psychological sickness. Psychological well being information science is central to my work, making this assembly a useful house to trade data, share experiences, and study from others within the discipline. I by no means miss it!

The occasion featured shows, panel discussions, and Q&A periods spanning profession phases and disciplines. A number of key themes emerged at this assembly which I consider replicate the route of journey inside psychological well being information science:

1. The position of Synthetic Intelligence (AI) in psychological well being information science

With AI quickly advancing, I used to be keen to listen to the way it’s being utilized inside psychological well being information science. Dr. Elizabeth Ford outlined how AI is at present getting used, from administrative purposes to forecasting affected person wants and predictive modelling to improvements reminiscent of AI-driven remedy and medical scribes. Whereas promising, important issues stay. Psychological well being information is usually extremely delicate, recorded in unstructured codecs, and might include surprising identifiers. This makes information safety and knowledgeable consent essential.

Public attitudes appear typically supportive of opt-out fashions if information is securely de-identified, however the nuances of psychological well being information—reminiscent of prior misdiagnoses and adjustments in diagnostic standards—pose challenges for AI interpretation. Bias in information, notably concerning LGBTQ+ people, homeless populations, and gender disparities, dangers reinforcing current inequalities.

Ford argued that whereas AI can help clinicians, closing choices ought to stay with human consultants to keep away from exacerbating biases or unintended penalties.

2. Early Profession Researcher Insights

For me, the early profession researcher (ECR) flash shows are all the time the spotlight of MQ conferences. Showcasing the following technology of expertise in psychological well being information science affords a invaluable glimpse into the rising developments and future route of the sphere.

One key theme that emerged from the ECR shows was the recurring problem in psychological well being information science of polypharmacy and nuanced prescribing patterns. Flash talks touched on many features of this problem from assessing drug interactions (e.g. metformin and antipsychotic-induced weight acquire), to the applying of huge language fashions to measure patterns in antidepressant remedy. Large efforts are being made to grasp one of the best ways to leverage prescribing information inside psychological well being information science.

3. Machine studying versus conventional epidemiology

Professor Honghan Wu examined how deep studying fashions carry out on psychological well being prediction duties, combining structured digital well being information with unstructured textual content. Unstructured textual content inside well being information is an enormous, considerably untapped, useful resource inside psychological well being information science. A panel dialogue in direction of the top of the afternoon about using scientific textual content in analysis sparked a lot debate, notably round machine studying vs conventional epidemiology. One key query emerged: Can AI outperform typical strategies when coping with complicated datasets? The consensus gave the impression to be that machine studying has benefits on the subject of dealing with giant quantities of knowledge however nonetheless wants cautious oversight to make sure essential nuance just isn’t missed.

4. Knowledge Sharing & the Way forward for Psychological Well being Science

Professor Andrew McIntosh gave a compelling discuss on the way forward for collaborative psychological well being analysis within the UK. He offered challenges in information harmonization, noting that rising dataset sizes via collaboration has unlocked insights into genetic underpinnings of psychiatric problems. The dialogue emphasised the significance of replicability, sufficient pattern sizes, and the invaluable efforts by organizations like MQ and DATAMIND to enhance information governance.