Machine learning to predict the development of psoriatic arthritis
- Intake: OxKEN
This project will use routinely collected clinical data from electronic medical records across Europe to develop machine learning algorithms for the identification of subjects at high risk of developing psoriatic arthritis amongst those with a diagnosis of psoriasis.
The data have all been previously mapped to a common data model (as used in the EHDEN project - www.ehden.eu) and so can be combined for federated analysis. Large databases of primary care electronic medical records including >20 million UK subjects (CPRD GOLD and AURUM) will be used to develop machine learning algorithms. Embedded within the large epidemiology group led by Dani, you will be taught how to apply different machine learning methods including Regularized logistic regression, Random forests, Gradient boosting machines, Decision trees, Naive Bayes, K-nearest neighbours, Neural networks and Deep learning (Convolutional neural networks, Recurrent neural network and Deep nets) methods.
The best performing algorithms will be made available to the community in an interactive web environment (see here for an example of prediction algorithms for the identification of subjects with rheumatoid arthritis at risk of infections, cardiovascular disease, and cancer).
This work will feed into a large European consortium aiming to predict the development of PsA and will inform future projects including development of an interventional study aiming to prevent PsA in people with psoriasis. This work will particularly inform the identification of patients at increased risk for PsA by clarifying the optimal inclusion and exclusion criteria to define an at-risk population for the future interventional trial.
Disease inception, psoriasis, machine learning, epidemiology, psoriatic arthritis
Biostatistics, big data, epidemiology, machine learning, specialist psoriatic arthritis and combined rheum/derm clinics, presentations at national and international meetings, link into large European PsA consortium investigating predictors of PsA development.
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