AI Advances in ICU with an Emphasis on Sepsis Prediction: An Overview

Charithea Stylianides, Andria Nicolaou, Waqar Aziz Sulaiman, Christina-Athanasia Alexandropoulou, Ilias Panagiotopoulos, Konstantina Karathanasopoulou, George Dimitrakopoulos, Styliani Kleanthous, Eleni Politi, Dimitris Ntalaperas, Xanthi Papageorgiou, Fransisco Garcia, Zinonas Antoniou, Nikos Ioannides, Lakis Palazis, Anna Vavlitou, Marios S. Pattichis, Constantinos S. Pattichis, Andreas S. Panayides

Artificial intelligence (AI) is increasingly applied in a wide range of healthcare and Intensive Care Unit (ICU) areas to serve—among others—as a tool for disease detection and prediction, as well as for healthcare resources’ management. Since sepsis is a high mortality and rapidly developing organ dysfunction disease afflicting millions in ICUs and costing huge amounts to treat, the area can benefit from the use of AI tools for early and informed diagnosis and antibiotic administration. Additionally, resource allocation plays a crucial role when patient flow is increased, and resources are limited. At the same time, sensitive data use raises the need for ethical guidelines and reflective datasets. Additionally, explainable AI is applied to handle AI opaqueness. This study aims to present existing clinical approaches for infection assessment in terms of scoring systems and diagnostic biomarkers, along with their limitations, and an extensive overview of AI applications in healthcare and ICUs in terms of (a) sepsis detection/prediction and sepsis mortality prediction, (b) length of ICU/hospital stay prediction, and (c) ICU admission/hospitalization prediction after Emergency Department admission, each constituting an important factor towards either prompt interventions and improved patient wellbeing or efficient resource management. Challenges of AI applications in ICU are addressed, along with useful recommendations to mitigate them. Explainable AI applications in ICU are described, and their value in validating, and translating predictions in the clinical setting is highlighted. The most important findings and future directions including multimodal data use and Transformer-based models are discussed. The goal is to make research in AI advances in ICU and particularly sepsis prediction more accessible and provide useful directions on future work.