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Developing Reinforcement Learning and Artificial Intelligence Tools to Support Clinical Care Including Care for Women with Perimenopausal and Menopaus

开发强化学习和人工智能工具以支持临床护理，包括对围绝经期和更年期女性的护理

基本信息

批准号：
2889702
负责人：
金额：
--
依托单位：
University of Liverpool
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2889702
关键词：
Developing Reinforcement Learning Artificial Intelligence

项目摘要

This project is being developed by the University of Liverpool in partnership with Newson Health. Menopause is a significant event in a woman's life, which can negatively affect the quality of life and increase health concerns. Although menopause itself doesn't cause cancer, the transitional phase to menopause can present relations to complex diseases, including breast cancer and endometrial cancer. The advantages and drawbacks of Hormone Replacement Therapy (HRT) are also a subject of debate. This project aims to revolutionise clinical care for women experiencing perimenopausal and menopausal symptoms by leveraging advanced techniques in Machine Learning and Deep Learning.We aim to develop machine learning methods to better interpret and understand Newson Health's internal clinical data, which can provide clinically valuable insights and has the potential to improve patient care. We also aim to develop deep reinforcement learning methods to automatically learn optimal individualised treatment regimens from clinical data. We will explore the combination of supervised/unsupervised and reinforcement learning to leverage previously collected clinical data to aid the online learning process. Offline reinforcement learning and Generative AI tools will also likely play an important role in our development process. Our research has the potential to improve treatment outcomes and personalised care.

该项目由利物浦大学与纽森健康公司合作开发。更年期是妇女生命中的一个重要事件，它会对生活质量产生负面影响，并增加健康问题。虽然更年期本身不会导致癌症，但更年期的过渡阶段可能与复杂的疾病有关，包括乳腺癌和子宫内膜癌。激素替代疗法（HRT）的优点和缺点也是一个争论的主题。该项目旨在通过利用机器学习和深度学习的先进技术，为经历围绝经期和绝经期症状的女性提供革命性的临床护理。我们的目标是开发机器学习方法，以更好地解释和理解Newson Health的内部临床数据，这些数据可以提供有价值的临床见解，并有可能改善患者护理。我们还旨在开发深度强化学习方法，以从临床数据中自动学习最佳的个性化治疗方案。我们将探索监督/无监督和强化学习的组合，以利用以前收集的临床数据来帮助在线学习过程。离线强化学习和生成式AI工具也可能在我们的开发过程中发挥重要作用。我们的研究有可能改善治疗结果和个性化护理。