  The continuously increasing cost of the US healthcare system has received
significant attention. Central to the ideas aimed at curbing this trend is the
use of technology, in the form of the mandate to implement electronic health
records (EHRs). EHRs consist of patient information such as demographics,
medications, laboratory test results, diagnosis codes and procedures. Mining
EHRs could lead to improvement in patient health management as EHRs contain
detailed information related to disease prognosis for large patient
populations. In this manuscript, we provide a structured and comprehensive
overview of data mining techniques for modeling EHR data. We first provide a
detailed understanding of the major application areas to which EHR mining has
been applied and then discuss the nature of EHR data and its accompanying
challenges. Next, we describe major approaches used for EHR mining, the metrics
associated with EHRs, and the various study designs. With this foundation, we
then provide a systematic and methodological organization of existing data
mining techniques used to model EHRs and discuss ideas for future research. We
conclude this survey with a comprehensive summary of clinical data mining
applications of EHR data, as illustrated in the online supplement.
