Intelligent Electronic Health Systems

This chapter describes the construction of complex multiscale models for use with Electronic health records (EHRs), for so-called intelligent EHRs, and their use in the extraction of clinically useful information from the very large healthcare data sets for the improvement of patient outcomes. It discusses how learning can take place within a machine learning framework, which provides the optimal approach for quantifying the uncertainty associated with the noisy and missing data typical of large healthcare data sets. The chapter introduces exemplar research themes within the field of intelligent electronic health systems, considering a number of case studies to demonstrate the potential of such research: using the broad range of data sets within the EHR, for improving understanding of infectious disease. It also include: augmenting the EHR with sensor data, for continuous monitoring of high-risk ambulatory patients and EHRs in the developing world, for improving access to affordable healthcare.