* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project
York Institute for Health Research Lecture Series Health Risk Assessment for Chronic Disease Prevention and Management Predictive Modelling of Health Clusters for Chronic Disease Management Speakers: Yawen Xu, York University Ted Hains & Zhen Mei, Manifold Data Mining Inc. Abstract: Chronic diseases are an increasing global challenge and their impacts increase income inequalities and health spending (67% of health budget and 8% of GDP); deplete household health and represent a major fiscal risk. In Canada, 3 out of 5 Canadian older than 20 have a chronic disease and 4 of 5 are at risk. Extensive research has described the pathway of chronic diseases: Behavioural Risk Tobacco Diet Inactivity Alcohol Metabolic/Physiological High blood pressure Obesity Raised glucose Raised lipids While this research has provided policy and guidance in supporting the key issues of the underlying diseases, the actual determinants of health and related quality of life remain a multi-dimensional challenge. This important theme has become a pressing priority for WHO and CDC. We have been pursuing the following questions in our research project: What are the driving factors of consumer lifestyle and behaviour for “successful” chronic disease prevention and management, and an improving quality of life/wellness? How can we transform and model the metrics of the social determinants of health into easy to use tools that promote improved chronic-care outcomes? In this talk we’ll discuss health clusters of patients with chronic diseases, particularly diabetes and heart diseases. We’ll show that the patients are typically grouped into seven distinct clusters: Depressed, Gloomy, Anxious, Satisfied, Rejuvenated, Happy and Optimistic. Furthermore, we’ll introduce a predictive modelling technique for classifying a patient into one of these clusters, based on their demographics, mental health and stress, health outcomes, social connection, motivation and lifestyles. We’ll illustrate potential application of the health clusters and predictive models. A Behavior Recommendation System for Happiness Improvement using Contrast Pattern Mining and k-Nearest Neighbours Algorithms Speakers: Jason Chen Joy Choi Abstract: The pursuit of happiness is sometimes a life-long goal for human beings. However, some of the factors affecting how people feel are not apparent. More importantly, factors tend to be related to each other: one factor may reduce or enhance the impact of another. In order to distinguish the underlying differences between optimism and pessimism, we use contrast pattern mining on a data set that describes a large group of people with heart disease or diabetes to identify groups of behaviour factors that may change people's feeling from unhappy to happy or vice versa. With such information, we are able to build a behavior recommendation system that uses a k-nearest neighbors based algorithm to generate recommendations to adopt or avoid contrasting groups of behaviors. In our web-based recommendation system, upon completion of a questionnaire by a user, the k-nearest neighbors algorithm will be used to identify people that are similar to the user. Subsequently, global contrast patterns (i.e., the contrast patterns found over the whole data set) that match the identified similar people are used to find personalized behaviour recommendations that will have positive impacts on the user's feeling.