cah-fullsite

Collaborators

Nimrah Barber

Pauline Mccabe

Rayhan Bell

Jonah Davie

Koby Mays

Oliwier Watson

Maurice Irvine

Summary

Predicting disorders of mental health early and developing personalised interventions has major implications for clinical management and treatment. Yet, progress in early diagnosis and personalised treatment is compromised by heterogeneity in patient populations. This project aims to establish a cross-disciplinary team of researchers and research engineers that will work together to deliver and sustain digital data-driven, cost-effective healthcare solutions for mental health.

This work will build human capital for delivering digital healthcare solutions for early prediction and precision stratification of patients with mental health disorders. The approach is unique in bringing together interdisciplinary know-how from machine learning, neuroscience, clinical practice and industry to tackle the challenge of early detection and prediction of personalised trajectories for mental health disorders.

The competitive advantage of the work rests on using machine learning to develop predictive models and mine large-scale multimodal data to reveal biologically-relevant predictors invisible to the clinician’s eye. In particular, the proposed artificial intelligence systems are:

Optimised through the lens of biology to deliver interpretable predictors that link non-invasive data to pathology markers and generalise across populations
Tailored to target the challenge of large-scale clinical data that are often sparse, incomplete, non-standardised and unlabelled
Scalable to deliver a clinical decision support system that will provide intelligent guidance to clinicians informed by diverse data types

Project Objectives

This project will translate PPMs into a fully deployable clinical decision support system that will:

Help clinicians assign the right patient at the right time to the right diagnostic or treatment pathway
Improve patient well-being and reduce healthcare costs, as patients undergo fewer, less invasive, less expensive diagnostic tests
Guide patient selection for clinical trials to enhance their efficacy and pave the way to drug discovery

To deliver this system, the research will validate:

PPM interoperability across diverse populations and multimodal data types
PPM scalability to operate on healthcare digital data
PPM reproducibility and transparency that adheres to medical device software standards

In the longer term, the research team will test the impact of the system on patent well-being and healthcare costs by integrating PPMs in clinical trials, paving the way to novel biomarker and drug discovery for neurodegenerative disease.

AI for precision mental health: Data-driven healthcare solutions