Improving asthma care through personalised risk assessment and support from a conversational agent

通过个性化风险评估和对话代理的支持改善哮喘护理

• 批准号: EP/W002477/1
• 负责人: Rafael Calvo
• 金额: $ 96.54万
• 依托单位: Imperial College London
• 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=EP%2FW002477%2F1
• 关键词: Improving; asthma; care; through; personalised

Over 5.4 million people have asthma in the UK, and despite £1Billion a year in NHS spending on asthma treatment, the national mortality rate is the highest in Europe. One of the reasons for this statistic, is that risk is often dramatically underestimated by many with asthma. This leads to neglect of early care, poor control, and eventually, hospitalisation. Therefore, improving accurate risk assessment and reduction via relevant behaviour change among people with asthma could save lives and dramatically reduce health care costs. Our multi-sector and international team will aim to address this early-care gap by investigating a new type of low-cost, and scalable personalised risk assessment, combined with follow-up automated support for risk reduction. The technology will leverage artificial intelligence to calculate a personalised asthma risk score based on voice features and self-reported data. It will then provide personalised advice on actions that can be taken to lower risk followed by customised conversational guidance to support the process of healthy change.We envision our work will ultimately lead to a safe and engaging system where the patients are able to see their current risk of an asthma attack after answering a series of questions, akin to clinical history taking, and record their voice. They then get ongoing customised support from an automated coach on how to reduce that risk. Any progress they make will visibly lower their risk (presented, for example, as "Strengthening their shield"), in order to make their state of asthma control more tangible and motivating. The technology will be developed collaboratively with direct involvement from people with asthma and clinicians through co-design methods and regular feedback in order to ensure risk assessment, feedback and guidance are clinically sound, and delivered in a way that is autonomy-supportive, clear, useful, and engaging to patients.Similar risk assessment approaches have already proven successful for improving cardiovascular and mental health, but this will be the first time personalised risk assessment is applied to asthma and integrated with support from a conversational agent. The risk calculation and feedback will involve three novel approaches:1) A data-driven model of asthma risk drawing on routinely collected de-identified Electronic Health Record data which will be used to identify which factors most accurately predict asthma exacerbation.2) Machine learning techniques for detecting asthma risk from voice features (eg, wheezing, breath rate, coughing). 3) Natural Language Processing techniques for developing an autonomy-supportive conversational agent to support health behaviour change.The project will be based at Imperial College London with clinicians and researchers from organisations in the UK, the US, and Australia. The project will also be undertaken in partnership with YourMD Ltd which will facilitate running a pilot study within their commercial app which will provide access to sufficient data for proof-of-concept testing. This will allow the algorithms to use dialogue and voice data from a larger number of participants, and will also accelerate translation for future project phases.

在英国，超过540万人患有哮喘，尽管NHS每年在哮喘治疗上花费10亿英镑，但全国死亡率是欧洲最高的。这一统计数据的原因之一是，许多哮喘患者往往大大低估了风险。这导致忽视早期护理，控制不良，最终导致住院治疗。因此，通过改变哮喘患者的相关行为来改善准确的风险评估和降低风险，可以挽救生命并大幅降低医疗保健成本。我们的多部门和国际团队将致力于通过研究一种新型的低成本、可扩展的个性化风险评估，结合后续的自动化风险降低支持，来解决这一早期护理差距。该技术将利用人工智能来计算基于语音特征和自我报告数据的个性化哮喘风险评分。然后，它将提供个性化的建议，可以采取的行动，以降低风险，其次是定制的对话指导，以支持健康的变化过程。我们设想我们的工作最终将导致一个安全和引人入胜的系统，患者能够看到他们目前的哮喘发作的风险后，回答了一系列问题，类似于临床病史，并记录他们的声音。然后，他们从自动教练那里获得持续的定制支持，以降低这种风险。他们所取得的任何进展都将明显降低他们的风险（例如，呈现为“加强他们的盾牌”），以使他们的哮喘控制状态更加有形和激励。该技术将在哮喘患者和临床医生的直接参与下通过共同设计方法和定期反馈进行合作开发，以确保风险评估，反馈和指导在临床上是合理的，并以一种支持性，明确，有用和吸引患者的方式提供。类似的风险评估方法已经被证明可以成功改善心血管和心理健康，但这将是第一次将个人风险评估应用于哮喘，并与对话代理人的支持相结合。风险计算和反馈将涉及三种新方法：1）利用常规收集的去识别化电子健康记录数据的哮喘风险数据驱动模型，该数据将用于确定哪些因素最准确地预测哮喘急性发作。2）机器学习技术用于从声音特征（例如，喘息，呼吸频率，咳嗽）检测哮喘风险。3)自然语言处理技术，用于开发一种支持健康行为改变的会话代理。该项目将在伦敦帝国理工学院与来自英国，美国和澳大利亚组织的临床医生和研究人员合作。该项目还将与YourMD Ltd合作开展，这将有助于在其商业应用程序中进行试点研究，该应用程序将为概念验证测试提供足够的数据。这将允许算法使用来自更多参与者的对话和语音数据，并将加快未来项目阶段的翻译。

项目成果

Development of an Asthma Exacerbation Risk Prediction Model for Conversational Use by Adults in England.

• DOI: 10.2147/por.s424098
• 发表时间: 2023
• 期刊: Pragmatic and observational research
• 影响因子: 8.9

Positive-Pair Redundancy Reduction Regularisation for Speech-Based Asthma Diagnosis Prediction

基于语音的哮喘诊断预测的正对冗余减少正则化

• DOI: 10.1109/icassp49357.2023.10097087
• 发表时间: 2023
• 作者: Rizos G