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

利用初级保健数据研究人工智能/机器学习在精神健康疾病早期筛查中的应用

基本信息

批准号：
2300953
负责人：
金额：
--
依托单位：
University of Warwick
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2300953
关键词：
Investigating use Artificial Intelligence Machine

项目摘要

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)

使用初级保健/公共领域内可用的数据（例如电子健康记录/社交媒体）确定是否可以结合使用统计方法和机器学习来提供疾病发展的可靠预测。然后，这可以用来向初级保健从业者发出“危险信号”，表明需要进一步诊断/筛查以确定早期干预是否有益。这是建立在 Nichols 等人早期在 WMS 上的工作的基础上的。（2016）关于儿童/年轻人的抑郁症。第一步是复制这一点，看看人工智能/机器学习是否可以提高特异性/可靠性，然后再考虑其他疾病、年龄组和方法。一个子项目将确定此类应用程序需要达到哪些目标才能被初级保健人员接受，可能通过焦点小组进行。参考文献：Nichols, L.、Ryan, R.、Connor, C.、Birchwood, M. 和 Marshall, T. (2018)“年轻人抑郁症诊断预测模型的推导：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，卷。 12、没有。 3，第 444-455 页 [在线]。 DOI：10.1111/eip.12332。与 EPSRC 临床技术研究领域一致（不包括成像）