A Review on Assessing the Use of Artificial Intelligence and Machine Learning Algorithms to Analyze ICU Data for Early Prediction of Patient

Abstract

The intensive care unit, also known as the intensive therapy unit is one of the most sensitive areas in a healthcare organization, as the decisions made here may make a difference between life and death of a patient. The amount and detail of information that are collected about the patient in the ICU ranging from simple parameters such as temperature and blood pressure to investigations like X-rays and laboratory results can be overwhelming to the healthcare provider. Recently, the emergence of the AI and ML technologies introduced the ways to use this data to amplify the patients’ outcomes. There are several benefits of AI and ML technologies for the analysis of a significant amount of data collected in ICUs to compare patients’ conditions and identify their changes, as well as to personalize the treatment and supply chain to match patients’ needs with the available resources efficiently. These technologies have the potential to transform ICU practices due the capability of the algorithms involved in analyzing and interpreting large volumes of data much faster and accurately than is humanly possible. Such model can also detect the symptoms which suggest that the patient is getting worse so that appropriate action can be taken to prevent adverse effects. The use of AI can help improve the accuracy of patient care because, unlike mass-produced medicine, the treatment plan will be developed based on the client’s specific traits and situation. Furthermore, the optimization of the ICU utilization, in compliance with the data analysis, contributes to the overall health care provision and cost-effectiveness. This literature review presents an overview of the current state of the art in the application of AI and ML to the ICU context, and assess the strengths and weaknesses of the proposed solutions in order to establish the challenges that must be addressed.