Inferring the Interactions of Risk Factors from EHRs.
The wealth of clinical information provided by the advent of electronic health records offers an exciting opportunity to improve the quality of patient care. Of particular importance are the risk factors, which indicate possible diagnoses, and the medications which treat them. By analysing which risk factors and medications were mentioned at different times in patients' EHRs, we are able to construct a patient's clinical chronology. This chronology enables us to not only predict how new patient's risk factors may progress, but also to discover patterns of interactions between risk factors and medications. We present a novel probabilistic model of patients' clinical chronologies and demonstrate how this model can be used to (1) predict the way a new patient's risk factors may evolve over time, (2) identify patients with irregular chronologies, and (3) discovering the interactions between pairs of risk factors, and between risk factors and medications over time. Moreover, the model proposed in this paper does not rely on (nor specify) any prior knowledge about any interactions between the risk factors and medications it represents. Thus, our model can be easily applied to any arbitrary set of risk factors and medications derived from a new dataset.
University of Texas at Dallas, Richardson, TX, USA.
Travis
T
Goodwin
University of Texas at Dallas, Richardson, TX, USA.
Sanda M
SM
Harabagiu