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Clinical data for ambulatory care, which accounts for 90% of the nationshealthcare spending, is characterized by relatively small sample sizes oflongitudinal data, unequal spacing between visits for each patient, withunequal numbers of data points collected across patients. While deep learninghas become state-of-the-art for sequence modeling, it is unknown which methodsof time aggregation may be best suited for these challenging temporal usecases. Additionally, deep models are often considered uninterpretable byphysicians which may prevent the clinical adoption, even of well performingmodels. We show that time-distributed-dense layers combined with GRUs producethe most generalizable models. Furthermore, we provide a framework for theclinical interpretation of the models.

Time Aggregation and Model Interpretation for Deep Multivariate Longitudinal Patient Outcome Forecasting Systems in Chronic Ambulatory Care