Secondly, these methods depend on feature engineering to capture the sequential nature of patient data, which may not adequately leverage the temporal patterns of the medical events and their dependencies.
To prepare the data for modeling and prediction, the time series data of cost, visit and medical information were extracted in the form of fine-grain features (i. e., segmenting each time series into a sequence of consecutive windows and representing each window by various statistics such as sum).
Temporal patterns learned from medical, visit and cost data made significant contributions to the prediction performance.
The first component captures dynamic changes of patients status in the ICU using their time series data (e. g., vital signs and laboratory tests).
This study makes contributions to time series classification and early ICU mortality prediction via identifying and enhancing temporal feature engineering and reduction methods for similarity-based time series classification.