Learning Predictive Models from Integrated Healthcare Data: Extending Pattern-Based and Generative Models to Capture Temporal and Cross-Attribute Dependencies

Modeling the dependencies among multiple temporal attributes derived from integrated healthcare databases represents an unprecedented opportunity to support medical and administrative decisions. However, existing predictive models are not yet able to successfully anticipate health conditions based on multiple (sparse) time sequences derived from repositories of health-records. To tackle this problem, we propose new predictive models able to learn from an expressive temporal structure, a time-enriched item set sequence, which captures both temporal and cross-attribute dependencies. Revised pattern-based models and hidden Markov models are proposed to address the properties of the target integrative temporal structures. The conducted experiments hold evidence for the utility and accuracy of the proposed predictive models to anticipate health conditions, such as the need for surgeries.