Monitoring the Treatments and Interventions to Intensive Care Patients: A Study for Multidimensional Evaluation of Changes in Patient Test Results with Principal Component Analysis

Abstract

Medical doctors of today are challenged with increasingly large volumes of high-dimensional, heterogeneous, and unstructured data from various sources that pose significant challenges for manual analysis. However, this unstructured data is mainly vital for decision making but there exists a shortage of intelligent tools to extract the hidden knowledge. Given these facts, the application of machine learning methods in healthcare is a growing phenomenon. This paper explores machine learning approaches for interpreting large quantities of continuously acquired, multivariate patient-based medical laboratory data, in intensive care unit (ICU) settings. The research hypothesizes that