Abstract
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.
Keywords
- Multidisciplinary Topics and Applications: Health and Medicine
- Machine Learning: Applications
- Machine Learning: Feature Extraction, Selection and Dimensionality Reduction
- Machine Learning: Representation learning
