Unsupervised Probabilistic Models for Sequential Electronic Health Records

EHR repositories contain large amounts of wide-ranging patient and treatment information and are essential for the development of individualized treatments in the context of disease progression [14]. With the broad adoption of EHR in the US, a large variety of data types are now routinely collected over long periods of time. This has ushered in an era of research focused on the applications and development of data-analytic tools for mining historical records of medical data to drive novel insight. Broadly, the extraction of meaningful patterns through unsupervised learning [31, 23, 17, 10] and the prediction of outcomes through supervised learning [22, 43, 41, 40, 37, 16, 13, 34, 3] are two important directions. Unsupervised methods can be applied towards many different tasks, such as prediction, imputation, and simulation; and often contain a model of the underlying structure in the data [29]. This underlying structure is not directly observed and can lead to insights that are otherwise difficult to produce, especially for large and complex data sets.