Investigating the use of Artificial/Intelligence/Machine Learning for early screening of mental health disorders using primary care data

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Using data available within primary care/public area such as Electronic Health Records/Social Media establish if it is possible using a combination of statistical methods and machine learning to provide reliable prediction of disorder development. This could then be used to "red flag" to primary care practitioners that further diagnosis/screening is required to determine if early intervention is beneficial. This builds on earlier work at WMS by Nichols et al. (2016) on depression in children/young adults. The first step would be to replicate this and see if AI/ML could deliver improved specificity/reliability before considering other disorders, age groups and methods. A sub project would be establishing what such an application would need to achieve to be acceptable to primary care staff, possibly via focus groups. REFERENCE: Nichols, L., Ryan, R., Connor, C., Birchwood, M. and Marshall, T. (2018) 'Derivation of a prediction model for a diagnosis of depression in young adults: a matched case-control study using electronic primary care records', Early Intervention in Psychiatry, vol. 12, no. 3, pp. 444-455 [Online]. DOI: 10.1111/eip.12332.Alligns with the EPSRC research area in Clinical Technologies (excluding imaging)