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

利用初级保健/公共领域可获得的数据，如电子健康记录/社交媒体，确定是否有可能使用统计方法和机器学习的组合来提供对疾病发展的可靠预测。这可以用来向初级保健从业者发出“危险信号”，即需要进一步的诊断/筛查，以确定早期干预是否有益。这是建立在尼科尔斯等人早期在WMS所做工作的基础上的。(2016)关于儿童/年轻人中的抑郁症。第一步将是复制这一点，并在考虑其他疾病、年龄组和方法之前，看看AI/ML是否能够提供更好的特异性/可靠性。一个子项目将是确定这样的应用程序需要实现什么才能被初级保健工作人员接受，可能是通过重点小组。参考文献：Nichols，L.，Ryan，R.，Connor，C.，Birchwood，M.和Matt，T.(2018)，《年轻人抑郁症诊断的预测模型的推导：使用电子初级保健记录的匹配病例对照研究》，《精神病学早期干预》，第12卷，第3期，第444-455页[在线]。DOI：10.1111/eip.12332.与EPSRC临床技术研究领域(不包括成像)保持一致

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)