Can machine learning be used to predict antidepressant use outcome longitudinally?

This project aims to understand the long-term effects of antidepressant use in the adult population. There is currently a gap in the literature regarding the longitudinal effects of pharmaceutical interventions for depression, so there is not great clinical understanding of the impact of antidepressants on health outcomes long-term. For example, some longitudinal studies suggest that the symptoms of depression improve overall after long term antidepressant use, but this is reported in tandem with adverse psychological and physical health outcomes like weight gain, sexual dysfunction, and emotional numbness (Dehar et al. 2016). In the psychiatric literature, there is an impoverished understanding of depression causality, with multiple competing hypotheses suggesting genetic, psychological, neurochemical, and neurostructural correlates of the illness. This suggests that depression as an overarching umbrella term could include multiple phenotypes, that if captured, could explain different illness trajectories and predict differential health outcomes after pharmaceutical intervention.
The long-term effects of antidepressant use on a wide range of health outcomes should be explored to ascertain whether they are an optimal intervention for people who present with mild to moderate depression and anxiety.
This project will apply machine learning and modelling techniques to the Avon Longitudinal Study of Parents and Children (ALSPAC) data set, to develop predictive models of antidepressant use outcomes. For example, significant changes in patient symptom profiles may indicate cognitive, psychological, or behavioural changes after long-term antidepressant use.
Initial exploration of the ALSPAC data set will be undertaken using a standard data science workflow, to analyse and understand exposures relevant to antidepressant use outcomes. Advanced machine learning methods will be used, and potentially progressed, in order to optimise novelty of contribution. State of the art anomaly detection will be developed to identify exposures that significantly change outcome probability. Bayesian network models and causal learning methods will be developed to compute the probability of relevant exposures being causally related to measurable patient outcomes, such as behavioural, cognitive, psychological and lifestyle outcomes. Deep clustering algorithms will be used to sub-group patients together, to explore whether certain exposures have more predictive power in long-term mood outcomes than others and factors which may modify associations such as genetic profiles or engagement with psychological therapies .
The timeline of the project is still being planned, but the first year would include developing models and running them on test datasets, bespoke dataset construction from existing ALSPAC data, and ALSPAC data familiarity. The second year would be used to run iterations of optimised models and potentially replicate with other cohort studies, with the final year being used for thesis write up and communication of results to relevant clinical bodies such as psychiatrists, GPS, and policy makers through digital health / machine learning publications.
The novelty of the project lies in the application of machine learning techniques to ALSPAC data to track antidepressant outcomes over at least a decade. While ML techniques have been applied across a number of digital healthcare domains, their use for mental health monitoring, prediction, and understanding the clinical pathways of patients remains under-researched. This is critically important moving forward, as this work will feed into the increasing use of precision medicine and apps for mental health (e.g., digital phenotyping), whereby having a deeper understanding of the phenotypes underpinning depression and how we can analyse this quickly and efficiently could transform the way we intervene and offer healthcare.

Impact on Health and Care
The CDT primarily addresses the most pressing needs of nations such as the UK - namely the growth of expenditure on long term health conditions. These conditions (e.g. diabetes, depression, arthritis) cost the NHS over £70Bn a year (~70% of its budget). As our populations continue to age these illnesses threaten the nation's health and its finances.

Digital technologies transforming our world - from transport to relationships, from entertainment to finance - and there is consensus that digital solutions will have a huge role to play in health and care. Through the CDT's emphasis on multidisciplinarity, teamwork, design and responsible innovation, it will produce future leaders positioned to seize that opportunity.

Impact on the Economy
The UK has Europe's 2nd largest medical technology industry and a hugely strong track record in health, technology and societal research. It is very well-placed to develop digital health and care solutions that meet the needs of society through the creation of new businesses.

Achieving economic impact is more than a matter of technology. The CDT has therefore been designed to ensure that its graduates are team players with deep understanding of health and social care systems, good design and the social context within which a new technology is introduced.

Many multinationals have been keen to engage the CDT (e.g. Microsoft, AstraZeneca, Lilly, Biogen, Arm, Huawei ) and part of the Director's role will be to position the UK as a destination for inwards investment in Digital Health. CDT partners collectively employ nearly 1,000,000 people worldwide and are easily in a position to create thousands of jobs in the UK.

The connection to CDT research will strongly benefit UK enterprises such as System C and Babylon, along with smaller companies such as Ayuda Heuristics and Evolyst.

Impact on the Public
When new technologies are proposed to collect and analyse highly personal health data, and are potentially involved in life or death decisions, it is vital that the public are given a voice. The team's experience is that listening to the public makes research better, however involving a full spectrum of the community in research also has benefits to those communities; it can be empowering, it can support the personal development of individuals within communities who may have little awareness of higher education and it can catalyse community groups to come together around key health and care issues.

Policy Makers
From the team's conversations with the senior leadership of the NHS, local leaders of health and social care transformation (see letters from NHS and Bristol City Council) and national reports, it is very apparent that digital solutions are seen as vital to the delivery of health and care. The research of the CDT can inform policy makers about the likely impact of new technology on future services.

Partner organisation Care & Repair will disseminate research findings around independent living and have a track record of translating academic research into changes in practice and policy.

Carers UK represent the role of informal carers, such as family members, in health and social care. They have a strong voice in policy development in the UK and are well-placed to disseminate the CDTs research to policy makers.

STEM Education
It has been shown that outreach for school age children around STEM topics can improve engagement in STEM topics at school. However female entry into STEM at University level remains dramatically lower than males; the reverse being true for health and life sciences. The CDT outreach leverages this fact to focus STEM outreach activities on digital health and care, which can encourage young women into computer science and impact on the next generation of women in higher education.

For academic impact see "Academic Beneficiaries" section.