Temporal Dynamic Embedding for Irregularly Sampled Time Series
This addresses the challenge of irregularly sampled data in healthcare applications, offering an incremental improvement over existing methods.
The paper tackled the problem of handling sparse and irregularly sampled time series in healthcare by proposing temporal dynamic embedding (TDE), which allows neural networks to process data with varying variables over time, resulting in competitive or better performance than baselines and state-of-the-art methods with reduced training runtime on three clinical datasets.
In several practical applications, particularly healthcare, clinical data of each patient is individually recorded in a database at irregular intervals as required. This causes a sparse and irregularly sampled time series, which makes it difficult to handle as a structured representation of the prerequisites of neural network models. We therefore propose temporal dynamic embedding (TDE), which enables neural network models to receive data that change the number of variables over time. TDE regards each time series variable as an embedding vector evolving over time, instead of a conventional fixed structured representation, which causes a critical missing problem. For each time step, TDE allows for the selective adoption and aggregation of only observed variable subsets and represents the current status of patient based on current observations. The experiment was conducted on three clinical datasets: PhysioNet 2012, MIMIC-III, and PhysioNet 2019. The TDE model performed competitively or better than the imputation-based baseline and several recent state-of-the-art methods with reduced training runtime.