  We address the problem of predicting when a disease will develop, i.e.,
medical event time (MET), from a patient's electronic health record (EHR). The
MET of non-communicable diseases like diabetes is highly correlated to
cumulative health conditions, more specifically, how much time the patient
spent with specific health conditions in the past. The common time-series
representation is indirect in extracting such information from EHR because it
focuses on detailed dependencies between values in successive observations, not
cumulative information. We propose a novel data representation for EHR called
cumulative stay-time representation (CTR), which directly models such
cumulative health conditions. We derive a trainable construction of CTR based
on neural networks that has the flexibility to fit the target data and
scalability to handle high-dimensional EHR. Numerical experiments using
synthetic and real-world datasets demonstrate that CTR alone achieves a high
prediction performance, and it enhances the performance of existing models when
combined with them.
