This weblog was written for MQ by Dr Amy Ronaldson.
In Might 2025, the MQ DATAMIND Information 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 achieve 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 massive 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 area to change data, share experiences, and be taught from others within the subject. I by no means miss it!
The occasion featured shows, panel discussions, and Q&A periods spanning profession levels and disciplines. A number of key themes emerged at this assembly which I consider mirror the route of journey inside psychological well being information science:
The function 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 commonly extremely delicate, recorded in unstructured codecs, and may include sudden identifiers. This makes information safety and knowledgeable consent vital.
Public attitudes appear typically supportive of opt-out fashions if information is securely de-identified, however the nuances of psychological well being data—reminiscent of prior misdiagnoses and adjustments in diagnostic standards—pose challenges for AI interpretation. Bias in data, significantly concerning LGBTQ+ people, homeless populations, and gender disparities, dangers reinforcing present inequalities.
Ford argued that whereas AI can help clinicians, remaining selections ought to stay with human consultants to keep away from exacerbating biases or unintended penalties.
Early Profession Researcher Insights
For me, the early profession researcher (ECR) flash shows are all the time the spotlight of MQ conferences. Showcasing the subsequent technology of expertise in psychological well being information science provides a priceless glimpse into the rising tendencies and future route of the sector.
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 points of this problem from assessing drug interactions (e.g. metformin and antipsychotic-induced weight achieve), to the appliance of enormous language fashions to measure patterns in antidepressant remedy. Enormous efforts are being made to grasp one of the simplest ways to leverage prescribing information inside psychological well being information science.
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 data with unstructured textual content. Unstructured textual content inside well being data is a large, considerably untapped, useful resource inside psychological well being information science. A panel dialogue in the direction of the tip of the afternoon about the usage of scientific textual content in analysis sparked a lot debate, significantly round machine studying vs conventional epidemiology. One key query emerged: Can AI outperform standard strategies when coping with complicated datasets? The consensus gave the impression to be that machine studying has benefits in the case of dealing with massive quantities of information however nonetheless wants cautious oversight to make sure necessary nuance just isn’t missed.
Information Sharing & the Way forward for Psychological Well being Science
Professor Andrew McIntosh gave a compelling speak on the way forward for collaborative psychological well being analysis within the UK. He introduced challenges in information harmonization, noting that rising dataset sizes by collaboration has unlocked insights into genetic underpinnings of psychiatric problems. The dialogue emphasised the significance of replicability, satisfactory pattern sizes, and the invaluable efforts by organizations like MQ and DATAMIND to enhance information governance.
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