The Impact of Statistical Modeling on Predictive Healthcare Analytics

Healthcare analytics has become a powerful umbrella for statistical modeling in the healthcare domain. The current reflection is set to explore the unprecedented potential of enriching predictive healthcare analytics through tight integration with diverse statistical modeling tasks (e.g., classification, prediction, estimation, association). It is suggested that refined statistical modeling contributes to enhancing model prediction capacity. Comprehensive experiment consoles the effectiveness and universality of this integrated proposal in dealing with miscellaneous prediction decision-making tasks within the healthcare domain. Moreover, extending state-of-the-art purely predictive healthcare work, it sheds new light on using statistical models for generative textured prediction tasks.

Healthcare analytics represents a burgeoning inter-disciplinary domain, where massive healthcare data can be systematically analyzed to extract valuable insights hidden behind, revolutionizing the healthcare sector from disease diagnosis to personalized treatment. Right from its inception, healthcare analytics naturally attracts the ever-evolving statistical modeling researchers. Towards diverse critical healthcare predictions, a variety of predictive statistical models have been cautiously customized, such as survival analysis or logistic regression for early-readmission prediction, support vector machine for disease risk forecasting. The wide exploration of these specialized statistical models stimulates the development of innovative algorithms for tackling the challenging prediction tasks in healthcare analytics. Despite rapid advance, intricate prediction decision-making in the healthcare domain still remains an extremely challenging yet large spectrum of possibilities. Practically speaking, for a specific prediction task, diverse datasets may be provided with various measurement scales and potential target variables.