New Data Representation and Learning Models for Temporal Health Forecasting

用于时间健康预测的新数据表示和学习模型

• 批准号: RGPIN-2021-04386
• 负责人: Manashty, Alireza
• 金额: $ 1.75万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742562
• 关键词: New; Data; Representation; Learning; Models

This research proposal solves the problem of advancing temporal data representation and forecasting models in healthcare. The motivation of this research is to model long-term historical data in real-time to not only detect, but to forecast events and activities such as mortality and major diagnoses. The main challenges in health data science addressed in this proposal are health forecasting and machine learning explainability. Applying predictive analytics in healthcare may prevent patients' emergency health problems and reduce costs in the long-term. Accurate and timely anomaly predictions focusing on recent events can even save lives. Furthermore, it is becoming more important to make decisions transparent, understandable, and explainable in healthcare systems. Providing trusted virtual healthcare services remotely has become even more critical during this pandemic due to the risks involved with continuous physical contact with health providers. An important step will be to make temporal sequence forecasting methods explainable so that a physician and a model can work synergistically to effectively enhance healthcare services. The long-term objectives of this program are devising novel temporal health data representation and forecasting models for generative and recurrent block learning models. Advancing generative forecasting models by devising a generative time block data representation, a novel time-aware generative model, and an interpretable generative model are among the short-term objectives of this proposal. Furthermore, considering the latest advances in recurrent and block neural networks, we aim to improve recurrent block models by creating a recursive time block data model, a new recurrent block model for short-term health forecasting, and a hybrid recurrent generative model for long-term health forecasting. The outcome of this research will be novel and significant as the health forecasting using big data is still in the early stages. Preventing accidents and health problems (rather than detecting them) can be significantly more desirable for both governments and individuals. Mortality and diagnosis forecasting are crucial when ICU beds are limited (e.g. during the COVID-19 crisis). Long-term health forecasting will provide invaluable insights for physicians, individuals, and health-care providers. Preventative measures can be taken before an adverse outcome is detected. New policies can be created based on forecasted epidemics observed in each community (e.g., infectious diseases such as COVID-19 and substance abuse). Finally, interpretable machine learning models make it easier for physicians to trust and use AI assistants in their diagnoses, which in turn augments healthcare across Canada and the globe.

该研究方案解决了医疗保健领域的时态数据表示和预测模型的问题。这项研究的动机是对长期历史数据进行实时建模，不仅可以检测，而且可以预测死亡率和重大诊断等事件和活动。该提案中提到的健康数据科学的主要挑战是健康预测和机器学习的可解释性。在医疗保健中应用预测分析可以预防患者的紧急健康问题，并降低长期成本。针对近期事件的准确及时的异常预测甚至可以挽救生命。此外，在医疗保健系统中使决策透明，可理解和可解释变得越来越重要。在此大流行期间，由于与医疗服务提供者持续身体接触所涉及的风险，远程提供可信的虚拟医疗服务变得更加重要。一个重要的步骤将是使时间序列预测方法可解释，以便医生和模型可以协同工作，有效地提高医疗服务。 该计划的长期目标是为生成和循环块学习模型设计新的时间健康数据表示和预测模型。通过设计生成时间块数据表示、新颖的时间感知生成模型和可解释的生成模型来推进生成预测模型是该提案的短期目标之一。此外，考虑到递归神经网络和块神经网络的最新进展，我们的目标是通过创建递归时间块数据模型、用于短期健康预测的新递归块模型和用于长期健康预测的混合递归生成模型来改进递归块模型。这项研究的结果将是新颖和重要的，因为使用大数据进行健康预测仍处于早期阶段。预防事故和健康问题（而不是发现它们）对政府和个人来说都是更可取的。当ICU床位有限时（例如在COVID-19危机期间），死亡率和诊断预测至关重要。长期健康预测将为医生、个人和医疗保健提供者提供宝贵的见解。在检测到不良结果之前可以采取预防措施。可以基于在每个社区中观察到的预测流行病来创建新策略（例如，传染病，如COVID-19和药物滥用）。最后，可解释的机器学习模型使医生更容易在诊断中信任和使用人工智能助手，这反过来又增强了加拿大和地球仪的医疗保健。