Virtual Clinical Trial Emulation with Generative AI Models

使用生成式 AI 模型进行虚拟临床试验仿真

• 批准号: MR/X005925/1
• 负责人: Feng Dong
• 金额: $ 14.28万
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Research Grant
• 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=MR%2FX005925%2F1
• 关键词: Virtual; Clinical; Trial; Emulation; Generative

This research will apply newly emerging generative AI technology to transform biomedical and health research by enabling virtual clinical trial emulation with synthetic data. It will overcome key limitations in both Randomised Controlled Trials (RCTs) and observational studies. RCTs have long been considered as the "gold standard" to evaluate treatments and medicines. However, they are far from being able to answer all clinical questions. In addition to time and cost constraints, RCTs have significant limitations to generalise their findings as their scope is limited. In many situations conducting RCTs with real patients is logistically challenging or unethical due to their potentially harmful nature. This leaves a significant knowledge gap, for example, we still have very limited clinical guidelines about how to manage multi-morbidities. While observational studies can overcome the issues faced by RCTs by leveraging routinely collected data from real world, they are typically imbalanced across population, diseases and interventions; there are a significant amount of noise and missing measurements in the data, and we need lengthy time and significant effort to remove patient identifiable information from the data to protect privacy. More importantly, treatment choices and outcomes in real world clinical cases may depend on factors that are not measured within the data, which may invalidate the observational study. AI research has made great advances in creating new data. With new generative AI models, we can generate synthetic patient populations that faithfully preserve the statistical attributes of real populations. Compared with anonymised real data, synthetic data can be generated in unlimited volume while containing "zero" information about real individuals. Hence, they are in a much better position to overcome legal barriers in data protection and sharing. More importantly, experiments with synthetic data will allow clinical researchers to perform "virtual-trials" to gain quantitative insight into causal relations between treatment and its effect. This will enable prediction and comparison of hypothetical treatments to seek answers to important research questions that currently cannot be answered in real trials. The success of this adventurous and timely research will bring a landscape change to revolutionise future biomedical and health research by broadening its research agenda, liberating its restrictions, saving cost and time, leading to significant benefits to healthcare by speeding up new timelines for treatment discovery, addressing increasingly complex healthcare landscape in elderly population and multi-morbidity, and transforming regulatory and policy making process.

这项研究将应用新兴的生成AI技术，通过使用合成数据实现虚拟临床试验仿真来改变生物医学和健康研究。它将克服随机对照试验（RCT）和观察性研究的关键局限性。长期以来，RCT被认为是评价治疗和药物的“金标准”。然而，他们还远远不能回答所有的临床问题。除了时间和成本的限制，随机对照试验有显着的局限性，以概括他们的研究结果，因为他们的范围是有限的。在许多情况下，对真实的患者进行随机对照试验在逻辑上具有挑战性，或者由于其潜在的有害性质而不道德。这留下了一个重大的知识缺口，例如，我们仍然有非常有限的临床指南，关于如何管理多种疾病。虽然观察性研究可以通过利用从真实的世界中常规收集的数据来克服RCT所面临的问题，但它们通常在人群、疾病和干预措施之间不平衡;数据中存在大量噪音和缺失测量，我们需要漫长的时间和大量的努力来从数据中删除患者可识别的信息以保护隐私。更重要的是，真实的世界临床病例中的治疗选择和结局可能取决于数据中未测量的因素，这可能使观察性研究无效。人工智能研究在创建新数据方面取得了巨大进展。通过新的生成式AI模型，我们可以生成忠实保留真实的人群统计属性的合成患者人群。与匿名化的真实的数据相比，合成数据可以无限量地生成，同时包含关于真实的个体的“零”信息。因此，他们更有能力克服数据保护和共享方面的法律的障碍。更重要的是，使用合成数据的实验将允许临床研究人员进行“虚拟试验”，以获得对治疗及其效果之间因果关系的定量洞察。这将使预测和比较假设的治疗，以寻求答案的重要研究问题，目前无法回答在真实的试验。这项大胆而及时的研究的成功将带来一个景观变化，通过扩大其研究议程，解放其限制，节省成本和时间，通过加快治疗发现的新时间表，解决老年人口和多种疾病日益复杂的医疗保健景观，并改变监管和政策制定过程，从而为医疗保健带来重大利益，从而彻底改变未来的生物医学和健康研究。

项目成果

Efficient Generative Adversarial Dag Learning with No-Curl

使用 No-Curl 的高效生成对抗性 Dag 学习

• DOI: 10.2139/ssrn.4331205
• 发表时间: 2023
• 作者: Petkov H