Supervised and Unsupervised-Based Analytics of Intensive Care Unit Data

摘要

Resources and personnel availability in Intensive Care Units (ICUs) of hospitals are scarce and challenging to manage, particularly certain group of patients are more likely to be dead than alive after released from ICUs. There has been availability of ICU data, opening the door for performing analytical approach to uncover the trends and patterns for better policy and resource allocation decision towards improved outcome of the patients. In this paper, we explored MIMIC III dataset and applied supervised and unsupervised learning approaches to shed some lights on the complex underlying relationships between the patient's Length of Stay (LOS) and a number of attributes available from data. Our results indicate that neural network-based approaches perform the best for predicting the mortality outcome compared to other supervised and unsupervised approaches.