Predictive Modeling of Patient Outcomes Using AI in Intensive Care Units (ICUs)
Authors
-
Sam Lewis
Northern Institute of Technology
-
Alice Green
Horizon Labs
https://orcid.org/0009-0000-0000-0000
Abstract
The Intensive Care Unit (ICU) is a high-stakes environment characterized by a massive influx of real-time patient data from heterogeneous sources. Clinicians must make rapid, life-or-death decisions, often under immense pressure. This study focuses on developing an AI-driven predictive model to forecast patient deterioration 12 hours in advance. We utilize a Recurrent Neural Network (RNN) with LSTM cells to analyze multivariate time-series data, including continuous vital signs (e.g., heart rate, SpO2) and intermittent lab results. Unlike static scoring systems like APACHE II, our dynamic model captures temporal trends and complex interactions between physiological parameters. The model achieved an Area Under the Receiver Operating Characteristic (AUROC) curve of 0.89. By providing a reliable early warning score, this system can function as a crucial clinical decision support tool, enabling medical staff to allocate resources more effectively and launch proactive interventions before a critical event, such as septic shock or cardiac arrest, occurs.
Copyright (c) 2025 Sam Lewis, Alice Green (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.