Simultaneous Prognosis of Multiple Chronic Conditions from Heterogeneous EHR Data

With vast amount of medical records being digitized in recent years in the form of Electronic Health Records (EHRs), accurate and large scale automated prognosis of diseases has become a possibility. However, most existing works in disease prediction have focused on a single condition or a few related conditions. Such models do not account for the fact that multiple conditions may co-occur in patients and that patient symptoms may be indicative of more than one distinct disease. Further, most learning models do not account for all patient symptoms, physiological test results, nursing notes and any other patient history that may be available in EHRs. For example, medication data, clinical notes, discharge summaries, physiological test results etc. are all indicative of different patient conditions. Modeling such heterogeneous data simultaneously can improve automated prognosis. In this work, we propose to model such heterogeneous data simultaneously for multiple chronic disease prediction. Our prior work on multiple chronic disease prediction using clinical notes, primarily early nursing progress notes have suggested that such data are predictive and useful for prognosis.