1 Mining Electronic Health Records ( EHR ) : A Survey Do Not Circulate

patterns from unevenly spaced time-series. Such time-series are common in EHR data elements such as laboratories test results and vitals. They further proposed temporal logic relations to combine patterns generated from univariate time-series. Sacchi et al. [Sacchi et al. 2007] extended the temporal abstraction framework to generate temporal association rules (TARs). In TAR’s, the antecedent and the consequent both consist of temporal patterns generated using the temporal abstraction framework. Jin et al. [Jin et al. 2008] further extended the TAR framework, to generate rules for mining unanticipated episodes where certain event patterns unexpectedly lead to outcomes e.g. taking two medicines together sometimes causes an adverse reaction. Batal et al. [Batal et al. 1994] used the temporal abstraction framework to propose the Segmented Time Series Feature mining algorithm for identifying the frequent patterns from an unevenly sampled time-series. Such modeling techniques have their own set of challenges. Patterns generated from individual patient time series are susceptible to noise. Further, such patterns can be of uneven temporal duration. Dynamic Clustering: Clustering techniques have also been used to group EHR data. Ghassempour et al. [Ghassempour et al. 2014] used hidden Markov models (HMM) to cluster patient medical trajectories. In their approach, they used both categorical variables (diagnosis codes) and continuous variables (vitals and laboratories test results) for clustering. They first mapped each medical trajectory to an HMM and then used KL divergence to compute the distance between two HMM’s. Sequential Rule Mining: Researchers have explored sequential association rule mining techniques for identifying causal relationships between diagnosis codes. Hanauer and Ramakrishnan [Hanauer and Ramakrishnan ] identified strongly associated pairs of ICD-9 codes with varying numbers of strong temporal associations ranging from 1 day to 10 years apart. They observed interesting temporal relationships between hypothyroidism and shingles (herpes reactivation). Liao and Chen [Liao and Chen 2013] proposed a sequential pattern mining approach to mine sequences with a gap constraints. Such gaps represent the delay between two concepts. Hripsack et al. [Hripcsak and Albers 2013] measured lagged linear correlation between EHR variables and healthcare process events. In their analysis, they considered five common healthcare process events: inpatient admission, inpatient discharge, outpatient visit, emergency department visit and ambulatory surgery and computed their correlation with several EHR variables such as laboratory values and concepts extracted from clinical notes. Visualization: Research has also been carried out in analytical reasoning facilitated by advanced interactive visual interfaces. Several research has been carried out by highlighting the opportunities and associated challenges [Caban and Gotz 2015], cohort analysis and exploration [Zhang et al. 2014; Gotz et al. 2012a], exploring comorbidities [Gotz et al. 2012b; Sun et al. 2010], exploring concepts [Cao et al. 2011b], clinical decision support [Gotz et al. 2011b], cohort identification [Cao et al. 2011a], disease network visualization [Perer and Sun 2012] and temporal frequent event sequences [Perer and Wang 2014]. Overcoming Challenges: As there are no comparison groups present (i.e. no case and control) any exploration for causation of disease or outcome of interest is not possible. In this aspect, they are highly reminiscent of atemporal descriptive study designs. The major difference with atemporal descriptive studies being that for atemporal descriptive studies, only prevalence rates can be computed, while for ACM Computing Surveys, Vol. 1, No. 1, Article 1, Publication date: April 2016.