New Data Representation and Learning Models for Temporal Health Forecasting

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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.