Chronic conditions can be costly but also preventable as well as predictable. We develop a model to predict in the short term (2-3 years) the onset of one or more chronic conditions. Five chronic conditions are considered: heart disease, stroke, diabetes, hypertension and cancer.
Predicting Chronic Diseases From Healthcare Data-A Framework Based On Graph Theory And Social Network Measures
The study illustrates a framework to predict the progression of chronic diseases from a new perspective using graph theory and social network analysis methods.
Previous studies have documented the application of electronic health insurance claim data for health services research purposes.
Article Link 08-10-15
We consider the problem of clustering hospitals based on their case-mix distributions. Hospitals belong to the same cluster if they offer the same mix of services and have similar demand for those services. The cluster labels can be used to control for case-mix in hospital level analyses.
Joint prediction of chronic conditions onset: comparing multivariate probits with Multiclass Support Vector Machines
We consider the problem of building accurate models that can predict, in the short term (2-3 years), the onset of one or more specific chronic conditions at individual level.
Timely information and education can enhance the ability of consumers to make informed choices about their health, lifestyle and modifiable disease risk factors.
Adapting graph theory and social network measures on healthcare data – a new framework to understand chronic disease progression
The paper presents an approach that applies social network theory to understand chronic disease progression.
Submitted to the Australasian Workshop on Health Informatics and Knowledge Management
A patient-centric approach to healthcare leads to an informal social network among medical professionals.
This chapter presents a research framework to:
View Paper 18-03-15
The healthcare sector deals with large volumes of electronic data related to patient services.
View Paper 18-03-15
Fei Wang, Sanjay Chawla, and Wei Liu. “Tikhonov or lasso regularization: Which is better and when. In Tools with Artificial Intelligence” (ICTAI), 2013 IEEE 25th International Conference on, pages 795–802. IEEE, 2013.
View paper 18-03-15
Sanjay Chawla, Federico Girosi, Fei Wang “Data Science and the Policy Completion Problem” The link between policy analysis and data science is more delicate than it may appear.
Full Article 16-03-15
Governments as well as health and accident compensation insurers are grappling to improve health outcomes while keeping spiralling costs under control.
The health industry consumes vast amounts of money and resources and is seen as a “black-hole” in many Government budgets. A ground breaking study using new science could save the industry millions and point the way for future research.