How would this smart expert system for pediatric intensive care medicine work?
Wulff: Our goal is to create a system that supports healthcare professionals in their daily work. More specifically, we would initially add a demonstrator module to the patient data management system: the smart decision support system. Based on machine learning algorithms, the system could then detect certain diseases and alert the medical staff. It is important to note that this is not an autonomous system, meaning it will not automatically start therapy or immediately administer medication. It is primarily designed to make a probable diagnosis.
Jack: Our project focuses on the detection of Systemic Inflammatory Response Syndrome (SIRS) and the diagnosis of severe (multiple) organ dysfunction syndrome and resulting organ failure - a problem that frequently occurs in the intensive care unit. SIRS shows similar symptoms to sepsis and is often associated with various diseases or interventions in critical care settings. SIRS is often not detected immediately, which is where the system would assist a diagnosis thanks to systematic and automated detection. The system always inquires whether the automated diagnosis was correct and learns autonomously based on the feedback. This self-learning function is the ultimate goal, which cannot be put into effect immediately. For now, the system is designed to support treatment through an early diagnosis, since many intensive care decisions are time critical. For example, if the patients rapidly develop kidney or liver failure during the post-operative period, early medication adjustments to reflect the situation could improve patient outcome.
What technologies does the project study or utilize?
Wulff: We basically have two principal components: rule-based processes and machine learning methods. Both methods are meant to play a role in the system, most likely resulting in a hybrid system. It is also important for the development to focus on open and standardized interfaces. The goal is to use the so-called interoperability standard, meaning to present the collected data in a way that allows access, exchange, and cooperative use of data in a coordinated manner, within and across organizational boundaries. This aspect is very important to us because if you don’t adhere to these standards, you inevitably create point solutions that only work in one hospital.
What are the next steps?
Jack: We are currently in the middle of the first half of the project and are working closely to implement further aspects of SIRS and organ dysfunctions into rule-based machine learning. This includes renal or hematological dysfunction, for example. We continue to train the system to recognize more criteria for the diseases. Every new identified rule means we conduct a new study to show that the system interprets the data correctly and accurately captures the target criteria.