The project is structured along three main aims:
The primary aim is to implement ready-to-use organ failure alarm systems and evaluate them in a prospective observational clinical study. The researchers will develop the ICU patient data visualizer software, presenting ICU data in a similar format to the bedside data. They will also create approaches to self-monitor the alarm systems to detect unreliable predictions. The performance of the alarm system will be compared with human assessors, and treatment recommendations will be evaluated.
The second aim is to conduct a randomized, controlled clinical pilot trial to assess a live alarm system. The trial will also evaluate clinical stakeholders’ acceptance and adoption of the technology. The researchers estimate a sample size of 1,000 patients for the pilot trial to detect a meaningful reduction in circulatory and respiratory failure events.
The final aim is to advance the methodology for patient state modeling and deterioration prediction. The researchers plan to develop machine learning (ML) models using a comprehensive ICU dataset from a hospital in Bern. These models will characterize a patient’s real-time health state based on various medical time series data. The goal is to create representations that summarize the patient’s medical history, predict near-future health events, and differentiate physiological subsystems. By leveraging AI techniques and mining large ICU datasets, the researchers aim to develop precise patient state representations that can predict health-state changes such as circulatory or respiratory failure.