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FAIR TREATMENT: Federated analytics and AI Research across TREs for AdolescenT MENTal health

公平对待：跨 TRE 的联合分析和人工智能研究促进青少年心理健康

基本信息

批准号：
MC_PC_21025
负责人：
Anna Moore
金额：
$ 43.67万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Intramural
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=MC_PC_21025
关键词：
FAIR TREATMENT Federated analytics AI

项目摘要

Negative aspects of a young person's life can lead to poor mental health (MH). However, services are stretched so often intervene late, leaving young people to suffer with longer lasting / more severe problems. It is possible to spot patterns showing who needs professional help early. However this is difficult as the information needed is secured in different places (e.g.health, education, social care records and falls under the remit of different research councils (MRC, ESRC). The main problems are:1) predictive models aren’t accurate enough: difficulties linking the above data together probably result in many factors being missed;2) models built in one place may not be effective in others: we need a way to securely analyse data from different places;3) there is no agreement on how to make sure data are managed safely, fairly and transparently. To solve these problems we will:1) combine two new technologies to demonstrate it is possible to analyse data across trusted research environments in different places and preserve individual’s privacy;2) consult with patients, the public, organisations contributing data, and legal/ethics experts to agree the best way to oversee data use, ensuring it’s managed safely and fairly.We can start quickly as we have been working together for three years and have already been funded to bring data together from education, social care and health services in Cambridgeshire and Peterborough, and the necessary ethical permissions are in place.

年轻人生活中的消极方面可能导致心理健康状况不佳（MH）。然而，服务捉襟见肘，往往干预晚，使年轻人遭受更持久/更严重的问题。这是有可能发现的模式，显示谁需要专业帮助早期。然而，这是困难的，因为所需的信息是在不同的地方（e.g.health），教育，社会护理记录和福尔斯属于不同的研究委员会（MRC，ESRC）的职权范围。主要问题是：1）预测模型不够准确：难以将上述数据联系在一起可能会导致许多因素被遗漏;2）在一个地方建立的模型可能在其他地方无效：我们需要一种方法来安全地分析来自不同地方的数据;3）对于如何确保数据得到安全，公平和透明的管理没有达成一致。为了解决这些问题，我们将：1）结合联合收割机和其他两种新技术，以证明在不同地方的可信研究环境中分析数据并保护个人隐私是可能的; 2）与患者、公众、提供数据的组织和法律的/伦理专家协商，以商定监督数据使用的最佳方式，我们可以很快开始，因为我们已经合作了三年，并且已经获得了将数据汇集在一起的资金从剑桥郡和彼得伯勒的教育，社会护理和医疗服务，必要的道德许可已经到位。