  Patient time series classification faces challenges in high degrees of
dimensionality and missingness. In light of patient similarity theory, this
study explores effective temporal feature engineering and reduction, missing
value imputation, and change point detection methods that can afford
similarity-based classification models with desirable accuracy enhancement. We
select a piecewise aggregation approximation method to extract fine-grain
temporal features and propose a minimalist method to impute missing values in
temporal features. For dimensionality reduction, we adopt a gradient descent
search method for feature weight assignment. We propose new patient status and
directional change definitions based on medical knowledge or clinical
guidelines about the value ranges for different patient status levels, and
develop a method to detect change points indicating positive or negative
patient status changes. We evaluate the effectiveness of the proposed methods
in the context of early Intensive Care Unit mortality prediction. The
evaluation results show that the k-Nearest Neighbor algorithm that incorporates
methods we select and propose significantly outperform the relevant benchmarks
for early ICU mortality prediction. 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.
