New Data Representation and Learning Models for Temporal Health Forecasting

这一研究方案解决了医疗保健中时态数据表示和预测模型的改进问题。这项研究的动机是对长期历史数据进行实时建模，不仅是为了检测，而且是为了预测事件和活动，如死亡率和重大诊断。这项提案中涉及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。将预测性分析应用于医疗保健可能会预防患者的紧急健康问题，并从长远来看降低成本。对最近发生的事件进行准确和及时的异常预测甚至可以拯救生命。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。在这次大流行期间，远程提供可信的虚拟医疗服务变得更加关键，因为与医疗服务提供者持续的身体接触涉及的风险。重要的一步将是使时间序列预测方法可解释，以便医生和模型能够协同工作，有效地增强医疗服务。该计划的长期目标是为生成性和递归的块学习模型设计新的时间健康数据表示和预测模型。通过设计生成性时间块数据表示来推进生成性预测模型、新的时间感知生成性模型和可解释的生成性模型是本提案的短期目标之一。此外，考虑到递归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的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有意义的，因为使用大数据的健康预测仍处于早期阶段。对政府和个人来说，预防事故和健康问题(而不是发现它们)可能要好得多。当重症监护病房床位有限时(例如在新冠肺炎危机期间)，死亡率和诊断预测至关重要。长期健康预测将为医生、个人和卫生保健提供者提供宝贵的见解。可以在检测到不良后果之前采取预防措施。可以根据在每个社区观察到的预测流行病(例如，新冠肺炎等传染病和药物滥用)来制定新的政策。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，这反过来又增加了加拿大和全球的医疗保健。

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.