Title: PPMF: A Patient-based Predictive Modeling Framework for Early ICU Mortality Prediction,
Abstract: To date, developing a good model for early intensive care unit (ICU)
mortality prediction is still challenging. This paper presents a patient based
predictive modeling framework (PPMF) to improve the performance of ICU
mortality prediction using data collected during the first 48 hours of ICU
admission. PPMF consists of three main components verifying three related
research hypotheses. The first component captures dynamic changes of patients
status in the ICU using their time series data (e.g., vital signs and
laboratory tests). The second component is a local approximation algorithm that
classifies patients based on their similarities. The third component is a
Gradient Decent wrapper that updates feature weights according to the
classification feedback. Experiments using data from MIMICIII show that PPMF
significantly outperforms: (1) the severity score systems, namely SASP III,
APACHE IV, and MPM0III, (2) the aggregation based classifiers that utilize
summarized time series, and (3) baseline feature selection methods.