Intelligent Healthcare Systems for Large-scale Populations

面向大规模人群的智能医疗系统

• 批准号: MR/S003916/2
• 负责人: Yang Long
• 金额: $ 14.52万
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Fellowship
• 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=MR%2FS003916%2F2
• 关键词: Intelligent; Healthcare; Systems; Large; scale

This project will be dedicated to developing and applying core AI technologies for general health data sciences. So far, I have developed three workable AI models for detecting infant stroke on a small dataset of accelerometer data collected by the Institute of Neuroscience, Newcastle University. Additionally, I am an expert in deep learning and am experienced at dealing with large-scale complex multi-modal data. My expertise in Zero-shot Learning focuses on addressing model interpretation and data insufficiency problems. I have additional strength in programming and software engineering. The project can be summarised into two main stages. The first stage focuses on proof-of-concept studies on two historical datasets, UK Biobank and NE 85+. UK Biobank has provided baseline measurements (such as the eye measures and saliva samples). In addition to the baseline assessment, 100,000 UK Biobank participants have worn a 24-hour activity monitor for a week, 20,000 of whom have undertaken repeated measures. A programme of online questionnaires is being rolled out (diet, cognitive function, work history and digestive health) and UK Biobank has embarked on a major study to scan (image) 100,000 participants (brain, heart, abdomen, bones & carotid artery). UK Biobank is linking to a wide range of electronic health records (cancer, death, hospital episodes, general practice), and is developing algorithms to accurately identify diseases and their subsets. Blood biochemistry is being analysed (such as hormones & cholesterol). Genotyping has been undertaken on all 500,000 participants and these data are being used in health research. In NE 85+, a total of 484 participants aged 87-89 years recruited to the study completed a purpose-designed physical activity questionnaire (PAQ), which categorised participants as mildly active, moderately active and very active. Out of them, 337 participants wore a triaxial accelerometer on the right wrist over a 5-7-day period to obtain objective measures. Data from subjective and objective measurement methods were compared. The first stage of the project utilising the NE 85+ data aims to integrate these complex data, e.g. MRI, accelerometer, and electronic health records. The project development will follow a simple-to-complex logistic. Initially, only one disease and one factor will be considered. Subsequently, multiple factors will be simultaneously considered. The model will then be progressively upgraded to take into account different health data sources and the correlations between different diseases. At this stage, we focus on disease diagnosis and alert, i.e. prediction of the risks of diseases based on observed factors. After stable performance has been achieved, the model will be focused on the rationale study. For example, what lifestyle or other factors can result in a high risk of heart disease? What is the attribute that we can change to reduce such a risk? Beside these rationale studies and healthcare feedback, visualisation techniques can provide more qualitative results that can help medical experts to discover new knowledge.During the second stage, stable AI models can be packaged into apps, with objective of encouraging more participants to engage in the study. Through smartphone or wearable sensors, participants can get access to direct healthcare from the cloud-based AI server. In turn, the collected data will be used to upgrade the model, validate previous studies, and large-scale cohort clinical study. More details can be found in the technical summary.

该项目将致力于开发和应用通用健康数据科学的核心人工智能技术。到目前为止，我已经开发了三个可行的人工智能模型，用于在纽卡斯尔大学神经科学研究所收集的加速度计数据的小数据集上检测婴儿中风。此外，我是深度学习专家，在处理大规模复杂的多模态数据方面经验丰富。我在Zero-shot Learning方面的专长主要集中在解决模型解释和数据不足问题。我在程序设计和软件工程方面更有特长。该项目可以概括为两个主要阶段。第一阶段的重点是两个历史数据集，英国生物银行和NE 85+的概念验证研究。英国生物银行提供了基线测量（如眼睛测量和唾液样本）。除了基线评估外，10万名英国生物银行参与者还佩戴了24小时活动监测器一周，其中2万人进行了重复测量。一项在线问卷调查计划正在推出（饮食，认知功能，工作历史和消化健康），英国生物银行已开始进行一项重大研究，对10万名参与者进行扫描（成像）（大脑，心脏，腹部，骨骼和颈动脉）。英国生物银行正在链接到广泛的电子健康记录（癌症，死亡，医院事件，全科医生），并正在开发算法来准确识别疾病及其子集。正在分析血液生化（如激素和胆固醇）。对所有50万名参与者进行了基因分型，这些数据正在用于健康研究。在NE 85+中，共有484名年龄在87-89岁之间的参与者参与了这项研究，他们完成了一份专门设计的身体活动问卷（PAQ），将参与者分为轻度活跃，中度活跃和非常活跃。其中，337名参与者在5-7天的时间内在右手腕上佩戴三轴加速度计，以获得客观的测量结果。比较了主观和客观测量方法的数据。该项目的第一阶段利用NE 85+数据，旨在整合这些复杂的数据，例如MRI、加速计和电子健康记录。项目开发将遵循从简单到复杂的逻辑。最初，只考虑一种疾病和一个因素。因此，将同时考虑多个因素。该模型随后将逐步升级，以考虑到不同的健康数据来源和不同疾病之间的相互关系。在这一阶段，我们专注于疾病诊断和预警，即根据观察到的因素预测疾病的风险。在达到稳定性能后，将重点研究模型的基本原理。例如，什么样的生活方式或其他因素会导致心脏病的高风险？我们可以改变什么属性来降低这种风险？除了这些基本原理研究和医疗反馈外，可视化技术还可以提供更多定性结果，帮助医学专家发现新知识。在第二阶段，可以将稳定的AI模型打包到应用程序中，目的是鼓励更多参与者参与研究。通过智能手机或可穿戴传感器，参与者可以从基于云的AI服务器直接获得医疗保健。反过来，收集的数据将用于升级模型，验证先前的研究和大规模队列临床研究。更多细节见技术摘要。

项目成果

Generic compact representation through visual-semantic ambiguity removal

• DOI: 10.1016/j.patrec.2018.04.024
• 发表时间: 2019
• 期刊: Pattern Recognit. Lett.
• 作者: Yang Long;Yu Guan;Ling Shao

Discriminative Latent Semantic Graph for Video Captioning

• DOI: 10.1145/3474085.3475519
• 发表时间: 2021-08
• 期刊: Proceedings of the 29th ACM International Conference on Multimedia
• 作者: Yang Bai;Junyan Wang;Yang Long;Bingzhang Hu;Yang Song;M. Pagnucco;Yu Guan

2D Pose-Based Real-Time Human Action Recognition With Occlusion-Handling

• DOI: 10.1109/tmm.2019.2944745
• 发表时间: 2020-06-01
• 期刊: IEEE TRANSACTIONS ON MULTIMEDIA
• 影响因子: 7.3
• 作者: Angelini, Federico;Fu, Zeyu;Naqvi, Syed Mohsen
• 通讯作者: Naqvi, Syed Mohsen

Dynamic Graph Warping Transformer for Video Alignment

• 发表时间: 2021
• 作者: Junyan Wang;Yang Long;M. Pagnucco;Yang Song

Visual-Semantic Aligned Bidirectional Network for Zero-Shot Learning

用于零样本学习的视觉语义对齐双向网络

• DOI: 10.1109/tmm.2022.3145666
• 发表时间: 2023
• 期刊: IEEE Transactions on Multimedia
• 影响因子: 7.3
• 作者: Rui Gao;Xingsong Hou;Jie Qin;Yuming Shen;Yang Long;Li Liu;Zhao Zhang;Ling Shao
• 通讯作者: Ling Shao