Artificial intelligence and data science in multiple long-term conditions

Our research

Artificial intelligence and data science in multiple long-term conditions

Our research is focused on using artificial intelligence (AI), which is a type of computer technology that can think and learn like humans. AI is really good at noticing patterns, especially in big amounts of data. We use AI to look at electronic health records, which are digital versions of a patient’s medical history.

Student working at laptop

Aims

Our Goal

The main goal of our research is to find patterns that can tell us when people might get sick or have health problems, even before these issues become serious. We look for these patterns in two ways:

1. Population Level

This means we try to find health risks that affect a lot of people, like how often a certain disease happens in a specific area.

2. Individual Level

This means we look at each person’s records to predict their personal health risks, like if someone is more likely to get diabetes because of their health history.

Patterns

How we can help

By finding these patterns, we can help doctors and nurses do a few important things:

 

Spot health problems early: This helps them treat patients before their conditions get worse.

 

Plan better care for patients: Especially for those with many long-term health issues, which helps save time and resources.

Meet the Data and AI theme memebers

Prorfessor Nick Reynolds

Artificial intelligence (AI) and data sciences in multiple long-term conditions Co-Theme Lead

Professor Michael R Barnes, PhD

Artificial intelligence (AI) and data sciences in multiple long-term conditions Co-Theme Lead

Dr Ayesha Sahar

Artificial intelligence (AI) and data sciences in multiple long-term conditions, Research Associate

Zainab Awan

Artificial intelligence (AI) and data sciences in multiple long-term conditions, Data Scientist

Shaurya Pal

Artificial intelligence (AI) and data sciences in multiple long-term conditions, Research Assistant

Liyuan Zhu

Artificial intelligence (AI) and data sciences in multiple long-term conditions, PhD Student

Henry Song

Artificial intelligence (AI) and data sciences in multiple long-term conditions, PhD Student

Secure Data Environment

To make sure all of this data is safe and private, we are setting up a special system called a Secure Data Environment (SDE). This system lets approved researchers look at health records without seeing any personal details, like names or addresses. This is important to protect patients’ privacy.

Data and AI Updates

Our Theme News

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The NIHR Newcastle PSRC Team at the NIHR SafetyNet Symposium, Manchester 2025

My Experience at the NIHR SafetyNet Symposium

PhD Student Liyuan Zhu describes her experience at the October, NIHR SafetyNet Symposium, Manchester 2025

Right Treatment, Right Time – Psoriasis Assoication Annual Meeting 2025

Research Assistant, Shaurya Pal give a run down on the Psoriasis Association Annual Meeting, that took place in June 2025 hosted in London.

student research conference, featuring students presenting their work, research posters, and a stage for talks. logo for NIHR Newcastle PSRC and Newcastle University are featured in the image.

Reflections on the NIHR Newcastle PSRC/Pharmacy Event – 19th June

PhD Student Henry Song reveiws his experience of the 2nd Annual PSRC Pharmacy Conference 2025

Prof Nick Reynolds

Development of DeepMerkel

Determining the course and severity of aggressive skin cancers enabling medics to personalise treatment.

Dr Ayesha Sahar

Unveiling Patterns in Healthcare Data through Advanced Modeling.

Data modeling provides a solution by analyzing historical data to identify and predict these connections.

Artificial intelligence and data science in multiple long-term conditions Publications and Awards

 

Researchers from the Artificial intelligence and data science in multiple long-term conditions them have the following outputs and awards

Reynolds DJ, Mountain S, Bartle V, Remfry E, Barnes MR, Reynolds NJ, Thompson A

 

AI MULTIPLY.  Targeting everyday decision makers in research: early career researcher and patient and public involvement and engagement collaboration in an AI-in-healthcare project. 

 

Patient and Public Involvement and Engagement (PPIE) is critical in the development and application of Artificial Intelligence (AI) in healthcare research to ensure that outcomes align with patients’ and the public’s needs. However, current PPIE practices often limit involvement to reactive tasks such as reviewing documents and providing plain English summaries. Whilst important, this approach can sideline PPIE from influencing key research decisions. Consequently, PPIE interactions often fail to adequately reach and influence everyday decision makers. On AI and big data research projects, these decisions are often made by Early Career Researchers (ECRs) who play a vital role in the day-to-day research process. After realising these limitations, and to address them, the NIHR-funded AI MULTIPLY consortium introduced twice-monthly “ECRs meet PPIE” sessions.

Alexander H, Malek R, Prieto-Merino D, Gribaleva E, Baden M, Beattie P, Brown S, Burton T, Cameron S, Coker B, Cork MJ, Hearn R, Ingram JR, Irvine AD, Johnston GA, Lambert A, Lunt M, Man I, Newell L, Ogg G, Patel P, Wan M, Warren RB, Woolf R, Yiu ZZN, Reynolds N, Ardern-Jones MR, Flohr C. 

 

A prospective observational cohort study comparing the treatment effectiveness and safety of ciclosporin, dupilumab and methotrexate in adult and paediatric patients with atopic dermatitis: results from the UK-Irish A-STAR register.

 

The main conventional systemic treatments for atopic dermatitis (AD) are methotrexate (MTX) and ciclosporin (CyA). Dupilumab was the first novel systemic agent to enter routine clinical practice. There are no head-to-head randomized controlled trials or real-world studies comparing these agents directly. Network meta-analyses provide indirect comparative efficacy and safety data and have shown strong evidence for dupilumab and CyA.

 

This research shows real-world comparison of CyA, dupilumab and MTX in AD suggests that dupilumab is consistently more effective than MTX and that CyA is most effective in very severe disease within 1 year of follow-up.

Al-Janabi A, Alabas OA, Yiu ZZ, Foulkes AC, Eyre S, Khan AH, Reynolds NJ, Smith CH, Griffiths CE, Warren RB, BADBIR Study Group.

 

Risk of Paradoxical Eczema in Patients Receiving Biologics for Psoriasis

 

Biologics used for plaque psoriasis have been reported to be associated with an atopic dermatitis (AD) phenotype, or paradoxical eczema, in some patients. The risk factors for this are unknown.

 

Incidence rates of paradoxical eczema, paradoxical eczema risk by biologic class, and the association of demographic and clinical variables with risk of paradoxical eczema were assessed using propensity score-weighted Cox proportional hazards regression models.

Hussain AB, Singleton G, Ball S, Weatherhead SC, Reynolds NJ, Hampton PJ.

 

Adopting a personalised approach to blood monitoring in psoriasis patients prescribed biologic therapy – a retrospective single centre review of real-world data.

 

With increasing evidence supporting earlier treatment intervention in patients with psoriasis and the growing number of available biosimilars it is likely that our prescribing of biologic therapies will continue to rise.1 Studies published from the British Association of Dermatologists Biologics and Immunomodulators Register group have demonstrated long-term safety of biologic therapies in psoriasis.2 Nevertheless, 6-monthly blood monitoring is performed for patients with psoriasis prescribed biologic therapies in most UK centres. This is largely guided by the British Association of Dermatologists 2020 guidelines for biologic therapy for psoriasis3 that recommends monitoring full blood count, creatinine and electrolytes and liver function tests at initially 3–4 months after starting therapy, then 6 monthly, or as clinically indicated. Six-monthly blood monitoring has significant healthcare cost implications, creates a burden on patient time and has a substantial environmental impact. However, patients with psoriasis requiring biologic therapies have a considerable burden of multiple long-term conditions that must be carefully considered when determining an individual’s blood monitoring frequency.

Motedayen Aval L, Yiu ZZN, Alabas OA, Griffiths CEM, Reynolds NJ, Hampton PJ, Smith CH, Warren RB; BADBIR Study Group.

 

Drug survival of IL-23 and IL-17 inhibitors versus other biologics for psoriasis: A British Association of Dermatologists Biologics and Immunomodulators Register cohort study.

 

Interleukin (IL)-23p19 and IL-17 inhibitors have demonstrated high efficacy for psoriasis in randomized controlled trials, though real-world data, particularly for risankizumab (IL-23p19 inhibitor) and brodalumab (IL-17 receptor (IL-17R) inhibitor), is limited.

 

Guselkumab and risankizumab have the most favourable drug survival for effectiveness, with comparable safety to ustekinumab, and more favourable than other BADBIR biologics. Longer drug survival may reduce treatment burden by minimizing treatment switches, clinic visits and disease flares, supporting IL-23p19 inhibitors as a practical long-term option for psoriasis.

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